2025-11-06 06:50:27,246 - main.py - DEBUG - 99 - loading sales data..
2025-11-06 06:50:28,105 - reading_data.py - DEBUG - 129 - start date: 2022-03-01 00:00:00 end date: 2025-06-30 00:00:00
2025-11-06 06:51:30,760 - functions.py - INFO - 274 - Fetched prediction months:
('2023-12-7', (2024, 1))
('2024-01-7', (2024, 2))
('2024-02-7', (2024, 3))
('2024-03-7', (2024, 4))
('2024-04-7', (2024, 5))
('2024-05-7', (2024, 6))
('2024-06-7', (2024, 7))
('2024-07-7', (2024, 8))
('2024-08-7', (2024, 9))
('2024-09-7', (2024, 10))
('2024-10-7', (2024, 11))
('2024-11-7', (2024, 12))
('2024-12-7', (2025, 1))
('2025-01-7', (2025, 2))
('2025-02-7', (2025, 3))
('2025-03-7', (2025, 4))
('2025-04-7', (2025, 5))
('2025-04-7', (2025, 6))
2025-11-06 06:51:31,163 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:51:31,262 - algorithm.py - DEBUG - 78 - shape: (52587, 279) start: 2022-05-02 00:00:00 end: 2023-12-06 00:00:00
2025-11-06 06:51:39,361 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:51:39,362 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:51:39,362 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:51:39,364 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 1) training data to: 2023-12-7
2025-11-06 06:51:39,364 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:51:49,504 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 1)
2025-11-06 06:51:49,506 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 1)
2025-11-06 06:51:49,507 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 1)
2025-11-06 06:51:49,507 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 1) training data to: 2023-12-7
2025-11-06 06:51:49,507 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:51:50,008 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 1)
2025-11-06 06:51:50,010 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 1)
2025-11-06 06:51:50,011 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 1)
2025-11-06 06:51:50,011 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:51:50,112 - algorithm.py - DEBUG - 78 - shape: (55164, 279) start: 2022-05-02 00:00:00 end: 2024-01-06 00:00:00
2025-11-06 06:51:58,747 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:51:58,748 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:51:58,748 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:51:58,750 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 2) training data to: 2024-01-7
2025-11-06 06:51:58,750 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:08,875 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 2)
2025-11-06 06:52:08,878 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 2)
2025-11-06 06:52:08,878 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 2)
2025-11-06 06:52:08,878 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 2) training data to: 2024-01-7
2025-11-06 06:52:08,878 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:09,407 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 2)
2025-11-06 06:52:09,409 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 2)
2025-11-06 06:52:09,410 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 2)
2025-11-06 06:52:09,410 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:52:09,518 - algorithm.py - DEBUG - 78 - shape: (58048, 279) start: 2022-05-02 00:00:00 end: 2024-02-06 00:00:00
2025-11-06 06:52:18,182 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:52:18,183 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:52:18,183 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:52:18,185 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 3) training data to: 2024-02-7
2025-11-06 06:52:18,185 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:28,185 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 3)
2025-11-06 06:52:28,187 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 3)
2025-11-06 06:52:28,188 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 3)
2025-11-06 06:52:28,188 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 3) training data to: 2024-02-7
2025-11-06 06:52:28,188 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:28,705 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 3)
2025-11-06 06:52:28,707 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 3)
2025-11-06 06:52:28,708 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 3)
2025-11-06 06:52:28,708 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:52:28,824 - algorithm.py - DEBUG - 78 - shape: (60793, 279) start: 2022-05-02 00:00:00 end: 2024-03-06 00:00:00
2025-11-06 06:52:37,472 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:52:37,473 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:52:37,473 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:52:37,475 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 4) training data to: 2024-03-7
2025-11-06 06:52:37,475 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:47,566 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 4)
2025-11-06 06:52:47,569 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 4)
2025-11-06 06:52:47,569 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 4)
2025-11-06 06:52:47,569 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 4) training data to: 2024-03-7
2025-11-06 06:52:47,569 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:52:48,072 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 4)
2025-11-06 06:52:48,074 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 4)
2025-11-06 06:52:48,074 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 4)
2025-11-06 06:52:48,074 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:52:48,193 - algorithm.py - DEBUG - 78 - shape: (63568, 279) start: 2022-05-02 00:00:00 end: 2024-04-06 00:00:00
2025-11-06 06:52:57,302 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:52:57,303 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:52:57,303 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:52:57,306 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 5) training data to: 2024-04-7
2025-11-06 06:52:57,306 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:07,367 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 5)
2025-11-06 06:53:07,370 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 5)
2025-11-06 06:53:07,370 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 5)
2025-11-06 06:53:07,370 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 5) training data to: 2024-04-7
2025-11-06 06:53:07,370 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:08,025 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 5)
2025-11-06 06:53:08,027 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 5)
2025-11-06 06:53:08,027 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 5)
2025-11-06 06:53:08,027 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:53:08,151 - algorithm.py - DEBUG - 78 - shape: (66230, 279) start: 2022-05-02 00:00:00 end: 2024-05-06 00:00:00
2025-11-06 06:53:17,438 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:53:17,439 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:53:17,439 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:53:17,441 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 6) training data to: 2024-05-7
2025-11-06 06:53:17,442 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:27,442 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 6)
2025-11-06 06:53:27,445 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 6)
2025-11-06 06:53:27,445 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 6)
2025-11-06 06:53:27,445 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 6) training data to: 2024-05-7
2025-11-06 06:53:27,445 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:27,946 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 6)
2025-11-06 06:53:27,948 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 6)
2025-11-06 06:53:27,948 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 6)
2025-11-06 06:53:27,948 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:53:28,082 - algorithm.py - DEBUG - 78 - shape: (68894, 279) start: 2022-05-02 00:00:00 end: 2024-06-06 00:00:00
2025-11-06 06:53:37,681 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:53:37,681 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:53:37,681 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:53:37,684 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 7) training data to: 2024-06-7
2025-11-06 06:53:37,684 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:47,726 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 7)
2025-11-06 06:53:47,729 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 7)
2025-11-06 06:53:47,729 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 7)
2025-11-06 06:53:47,729 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 7) training data to: 2024-06-7
2025-11-06 06:53:47,729 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:53:48,231 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 7)
2025-11-06 06:53:48,233 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 7)
2025-11-06 06:53:48,234 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 7)
2025-11-06 06:53:48,234 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:53:48,368 - algorithm.py - DEBUG - 78 - shape: (71778, 279) start: 2022-05-02 00:00:00 end: 2024-07-06 00:00:00
2025-11-06 06:53:57,990 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:53:57,990 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:53:57,990 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:53:57,993 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 8) training data to: 2024-07-7
2025-11-06 06:53:57,993 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:08,113 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 8)
2025-11-06 06:54:08,116 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 8)
2025-11-06 06:54:08,116 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 8)
2025-11-06 06:54:08,116 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 8) training data to: 2024-07-7
2025-11-06 06:54:08,116 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:08,643 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 8)
2025-11-06 06:54:08,645 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 8)
2025-11-06 06:54:08,646 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 8)
2025-11-06 06:54:08,646 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:54:08,786 - algorithm.py - DEBUG - 78 - shape: (74647, 279) start: 2022-05-02 00:00:00 end: 2024-08-06 00:00:00
2025-11-06 06:54:19,386 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:54:19,386 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:54:19,387 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:54:19,389 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 9) training data to: 2024-08-7
2025-11-06 06:54:19,389 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:29,464 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 9)
2025-11-06 06:54:29,467 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 9)
2025-11-06 06:54:29,467 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 9)
2025-11-06 06:54:29,467 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 9) training data to: 2024-08-7
2025-11-06 06:54:29,467 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:29,983 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 9)
2025-11-06 06:54:29,985 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 9)
2025-11-06 06:54:29,986 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 9)
2025-11-06 06:54:29,986 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:54:30,126 - algorithm.py - DEBUG - 78 - shape: (77566, 279) start: 2022-05-02 00:00:00 end: 2024-09-06 00:00:00
2025-11-06 06:54:40,175 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:54:40,176 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:54:40,176 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:54:40,178 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 10) training data to: 2024-09-7
2025-11-06 06:54:40,179 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:41,063 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 10)
2025-11-06 06:54:50,194 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 10)
2025-11-06 06:54:50,197 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 10)
2025-11-06 06:54:50,197 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 10)
2025-11-06 06:54:50,197 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 10) training data to: 2024-09-7
2025-11-06 06:54:50,197 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:54:50,246 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 10)
2025-11-06 06:54:50,705 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 10)
2025-11-06 06:54:50,708 - prediction_task.py - DEBUG - 31 - test data not found for 195 Kelheim in period (2024, 10)
2025-11-06 06:54:50,708 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 10)
2025-11-06 06:54:50,708 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:54:50,858 - algorithm.py - DEBUG - 78 - shape: (80206, 279) start: 2022-05-02 00:00:00 end: 2024-10-05 00:00:00
2025-11-06 06:55:01,267 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:55:01,267 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:55:01,268 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:55:01,271 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 11) training data to: 2024-10-7
2025-11-06 06:55:01,272 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:02,159 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 11)
2025-11-06 06:55:11,547 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 11)
2025-11-06 06:55:11,636 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 11)
2025-11-06 06:55:11,636 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 11) training data to: 2024-10-7
2025-11-06 06:55:11,636 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:11,687 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 11)
2025-11-06 06:55:12,148 - prediction_task.py - DEBUG - 31 - test data not found for 194 Michelfeld in period (2024, 11)
2025-11-06 06:55:12,153 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 11)
2025-11-06 06:55:12,153 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:55:12,303 - algorithm.py - DEBUG - 78 - shape: (83073, 279) start: 2022-05-02 00:00:00 end: 2024-11-06 00:00:00
2025-11-06 06:55:23,603 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:55:23,604 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:55:23,604 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:55:23,608 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2024, 12) training data to: 2024-11-7
2025-11-06 06:55:23,608 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:24,490 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 12)
2025-11-06 06:55:33,812 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2024, 12)
2025-11-06 06:55:33,812 - main.py - INFO - 137 - Successfully trained agp agp for month (2024, 12) training data to: 2024-11-7
2025-11-06 06:55:33,812 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:33,863 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2024, 12)
2025-11-06 06:55:34,339 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2024, 12)
2025-11-06 06:55:34,340 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:55:34,495 - algorithm.py - DEBUG - 78 - shape: (85947, 279) start: 2022-05-02 00:00:00 end: 2024-12-06 00:00:00
2025-11-06 06:55:45,797 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:55:45,797 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:55:45,797 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:55:45,800 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 1) training data to: 2024-12-7
2025-11-06 06:55:45,800 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:46,708 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 1)
2025-11-06 06:55:49,585 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 1)
2025-11-06 06:55:56,003 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 1)
2025-11-06 06:55:56,003 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 1) training data to: 2024-12-7
2025-11-06 06:55:56,003 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:55:56,054 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 1)
2025-11-06 06:55:56,202 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 1)
2025-11-06 06:55:56,519 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 1)
2025-11-06 06:55:56,519 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:55:56,688 - algorithm.py - DEBUG - 78 - shape: (88432, 279) start: 2022-05-02 00:00:00 end: 2025-01-06 00:00:00
2025-11-06 06:56:07,758 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:56:07,758 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:56:07,759 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:56:07,762 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 2) training data to: 2025-01-7
2025-11-06 06:56:07,762 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:56:08,645 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 2)
2025-11-06 06:56:11,454 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 2)
2025-11-06 06:56:17,560 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 2)
2025-11-06 06:56:17,561 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 2) training data to: 2025-01-7
2025-11-06 06:56:17,561 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:56:17,611 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 2)
2025-11-06 06:56:17,765 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 2)
2025-11-06 06:56:18,086 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 2)
2025-11-06 06:56:18,086 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:56:18,251 - algorithm.py - DEBUG - 78 - shape: (91428, 279) start: 2022-05-02 00:00:00 end: 2025-02-06 00:00:00
2025-11-06 06:56:29,297 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:56:29,297 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:56:29,297 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:56:29,300 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 3) training data to: 2025-02-7
2025-11-06 06:56:29,301 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:56:30,191 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 3)
2025-11-06 06:56:33,035 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 3)
2025-11-06 06:56:39,464 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 3)
2025-11-06 06:56:39,465 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 3) training data to: 2025-02-7
2025-11-06 06:56:39,465 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:56:39,515 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 3)
2025-11-06 06:56:39,661 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 3)
2025-11-06 06:56:39,972 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 3)
2025-11-06 06:56:39,972 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:56:40,135 - algorithm.py - DEBUG - 78 - shape: (94067, 279) start: 2022-05-02 00:00:00 end: 2025-03-06 00:00:00
2025-11-06 06:56:51,765 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:56:51,765 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:56:51,765 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:56:51,768 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 4) training data to: 2025-03-7
2025-11-06 06:56:51,769 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:56:52,677 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 4)
2025-11-06 06:56:55,908 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 4)
2025-11-06 06:57:02,183 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 4)
2025-11-06 06:57:02,183 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 4) training data to: 2025-03-7
2025-11-06 06:57:02,184 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:57:02,235 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 4)
2025-11-06 06:57:02,390 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 4)
2025-11-06 06:57:02,719 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 4)
2025-11-06 06:57:02,719 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:57:02,891 - algorithm.py - DEBUG - 78 - shape: (96946, 279) start: 2022-05-02 00:00:00 end: 2025-04-05 00:00:00
2025-11-06 06:57:14,347 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:57:14,348 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:57:14,348 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:57:14,351 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 5) training data to: 2025-04-7
2025-11-06 06:57:14,351 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:57:15,231 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 5)
2025-11-06 06:57:16,221 - prediction_task.py - DEBUG - 31 - test data not found for 62 Heidenheim in period (2025, 5)
2025-11-06 06:57:17,997 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 5)
2025-11-06 06:57:24,401 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 5)
2025-11-06 06:57:24,401 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 5) training data to: 2025-04-7
2025-11-06 06:57:24,402 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:57:24,453 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 5)
2025-11-06 06:57:24,506 - prediction_task.py - DEBUG - 31 - test data not found for 62 Heidenheim in period (2025, 5)
2025-11-06 06:57:24,599 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 5)
2025-11-06 06:57:24,921 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 5)
2025-11-06 06:57:24,921 - algorithm.py - DEBUG - 72 - start training with xgboost...
2025-11-06 06:57:25,092 - algorithm.py - DEBUG - 78 - shape: (96946, 279) start: 2022-05-02 00:00:00 end: 2025-04-05 00:00:00
2025-11-06 06:57:36,776 - algorithm.py - INFO - 122 - parameters {'objective': 'reg:squarederror', 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': 0.8, 'device': None, 'early_stopping_rounds': None, 'enable_categorical': True, 'eval_metric': <function mean_squared_error at 0x7fe076528310>, 'feature_types': None, 'gamma': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': 0.01, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': 7, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'multi_strategy': None, 'n_estimators': 1250, 'n_jobs': -1, 'num_parallel_tree': None, 'random_state': 42, 'reg_alpha': 0.05, 'reg_lambda': 1.0, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': 0.8, 'tree_method': 'hist', 'validate_parameters': None, 'verbosity': None}
2025-11-06 06:57:36,777 - algorithm.py - INFO - 130 - parameters:
{ 'colsample_bytree': 0.8,
'enable_categorical': True,
'eval_metric': <function mean_squared_error at 0x7fe076528310>,
'learning_rate': 0.01,
'max_depth': 7,
'missing': nan,
'n_estimators': 1250,
'n_jobs': -1,
'objective': 'reg:squarederror',
'random_state': 42,
'reg_alpha': 0.05,
'reg_lambda': 1.0,
'subsample': 0.8,
'tree_method': 'hist'}
2025-11-06 06:57:36,777 - algorithm.py - INFO - 131 - indep vars:
['ladenvkpmean', 'ladenvkpmin', 'ladenvkpmax', 'ladenvkpstd', 'fg_bl_BW', 'fg_bl_BY', 'fg_bl_HE', 'fg_bl_NW', 'fg_bl_RP', 'weekdayname_Monday', 'weekdayname_Tuesday', 'weekdayname_Wednesday', 'weekdayname_Thursday', 'weekdayname_Friday', 'weekdayname_Saturday', 'isoweek_1', 'isoweek_2', 'isoweek_3', 'isoweek_4', 'isoweek_5', 'isoweek_6', 'isoweek_7', 'isoweek_8', 'isoweek_9', 'isoweek_10', 'isoweek_11', 'isoweek_12', 'isoweek_13', 'isoweek_14', 'isoweek_15', 'isoweek_16', 'isoweek_17', 'isoweek_18', 'isoweek_19', 'isoweek_20', 'isoweek_21', 'isoweek_22', 'isoweek_23', 'isoweek_24', 'isoweek_25', 'isoweek_26', 'isoweek_27', 'isoweek_28', 'isoweek_29', 'isoweek_30', 'isoweek_31', 'isoweek_32', 'isoweek_33', 'isoweek_34', 'isoweek_35', 'isoweek_36', 'isoweek_37', 'isoweek_38', 'isoweek_39', 'isoweek_40', 'isoweek_41', 'isoweek_42', 'isoweek_43', 'isoweek_44', 'isoweek_45', 'isoweek_46', 'isoweek_47', 'isoweek_48', 'isoweek_49', 'isoweek_50', 'isoweek_51', 'isoweek_52', 'month_1', 'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7', 'month_8', 'month_9', 'month_10', 'month_11', 'month_12', 'year_2022', 'year_2023', 'year_2024', 'fg_sg_1', 'fg_sg_2', 'fg_sg_3', 'fg_sg_9', 'fg_sg_19', 'fg_sg_21', 'fg_sg_24', 'fg_sg_26', 'fg_sg_27', 'fg_sg_28', 'fg_sg_29', 'fg_sg_31', 'fg_sg_34', 'fg_sg_40', 'fg_sg_41', 'fg_sg_43', 'fg_sg_44', 'fg_sg_52', 'fg_sg_55', 'fg_sg_57', 'fg_sg_58', 'fg_sg_61', 'fg_sg_62', 'fg_sg_63', 'fg_sg_64', 'fg_sg_65', 'fg_sg_66', 'fg_sg_68', 'fg_sg_69', 'fg_sg_71', 'fg_sg_73', 'fg_sg_74', 'fg_sg_76', 'fg_sg_77', 'fg_sg_80', 'fg_sg_81', 'fg_sg_82', 'fg_sg_83', 'fg_sg_85', 'fg_sg_88', 'fg_sg_89', 'fg_sg_91', 'fg_sg_92', 'fg_sg_96', 'fg_sg_97', 'fg_sg_99', 'fg_sg_101', 'fg_sg_103', 'fg_sg_104', 'fg_sg_105', 'fg_sg_107', 'fg_sg_108', 'fg_sg_109', 'fg_sg_110', 'fg_sg_111', 'fg_sg_112', 'fg_sg_113', 'fg_sg_117', 'fg_sg_118', 'fg_sg_120', 'fg_sg_125', 'fg_sg_126', 'fg_sg_128', 'fg_sg_130', 'fg_sg_131', 'fg_sg_133', 'fg_sg_134', 'fg_sg_136', 'fg_sg_139', 'fg_sg_141', 'fg_sg_142', 'fg_sg_143', 'fg_sg_146', 'fg_sg_147', 'fg_sg_148', 'fg_sg_149', 'fg_sg_150', 'fg_sg_151', 'fg_sg_153', 'fg_sg_154', 'fg_sg_155', 'fg_sg_157', 'fg_sg_159', 'fg_sg_160', 'fg_sg_161', 'fg_sg_162', 'fg_sg_164', 'fg_sg_165', 'fg_sg_166', 'fg_sg_167', 'fg_sg_168', 'fg_sg_169', 'fg_sg_170', 'fg_sg_171', 'fg_sg_172', 'fg_sg_173', 'fg_sg_174', 'fg_sg_176', 'fg_sg_177', 'fg_sg_179', 'fg_sg_181', 'fg_sg_182', 'fg_sg_183', 'fg_sg_185', 'fg_sg_187', 'fg_sg_188', 'fg_sg_189', 'fg_sg_190', 'fg_sg_191', 'fg_sg_192', 'fg_sg_193', 'psv_starts', 'psv', 'salesday', 'presales', 'postsales', 'bridge_days', 'outlet', 'store_opening', 'weiberfasching_2022', 'faschingsfreitag_2022', 'faschingssamstag_2022', 'rosenmontag_2022', 'faschingdienstag_2022', 'weiberfasching', 'faschingsfreitag', 'faschingssamstag', 'pre_christmas_peaks', 'valentines_day', 'interaction', 'market_Montags', 'market_Dienstags', 'market_Mittwochs', 'market_Donnerstags', 'market_Freitags', 'market_Samstags', 'population', 'shop_area', 'dist_from_weiden', 'ratio', 'average', 'shop_count', 'weekday_special', 'weekend_special', 'x_coord', 'y_coord', 'dayofmonth', 'isoweek', 'month', 'dayofyear', 'dayofseason', 'season_month']
2025-11-06 06:57:36,780 - main.py - INFO - 137 - Successfully trained xgb9 xgboost for month (2025, 6) training data to: 2025-04-7
2025-11-06 06:57:36,781 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:57:37,655 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 6)
2025-11-06 06:57:38,630 - prediction_task.py - DEBUG - 31 - test data not found for 62 Heidenheim in period (2025, 6)
2025-11-06 06:57:40,393 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 6)
2025-11-06 06:57:46,794 - main.py - INFO - 145 - Successfully predicted with xgb9 xgboost for month (2025, 6)
2025-11-06 06:57:46,794 - main.py - INFO - 137 - Successfully trained agp agp for month (2025, 6) training data to: 2025-04-7
2025-11-06 06:57:46,794 - prediction_task.py - DEBUG - 21 - predictions starting...
2025-11-06 06:57:46,844 - prediction_task.py - DEBUG - 31 - test data not found for 29 Kelheim II in period (2025, 6)
2025-11-06 06:57:46,894 - prediction_task.py - DEBUG - 31 - test data not found for 62 Heidenheim in period (2025, 6)
2025-11-06 06:57:46,984 - prediction_task.py - DEBUG - 31 - test data not found for 96 Hof in period (2025, 6)
2025-11-06 06:57:47,295 - main.py - INFO - 145 - Successfully predicted with agp agp for month (2025, 6)
2025-11-06 06:57:47,295 - functions.py - INFO - 425 - NB: using calibrate2 to meet AGP monthly totals
2025-11-06 06:58:16,086 - functions.py - INFO - 87 - No duplicates found in predictions.
2025-11-06 06:58:51,426 - functions.py - DEBUG - 207 - metric: mae
2025-11-06 06:58:58,846 - functions.py - DEBUG - 207 - metric: mape
2025-11-06 06:59:06,255 - functions.py - DEBUG - 207 - metric: jensen_shannon
2025-11-06 06:59:13,645 - functions.py - DEBUG - 207 - metric: kullback_leibler
2025-11-06 06:59:23,188 - conversion_functions.py - DEBUG - 168 - business hours file: (1332, 9) Index(['fg_sg', 'fg_bez', 'Bundesland', 'year', 'month', 'weekday_hours', 'saturday_hours', 'weekday_hours_extra', 'saturday_hours_extra'], dtype='object')
2025-11-06 06:59:23,446 - conversion_functions.py - DEBUG - 168 - business hours file: (2664, 9) Index(['fg_sg', 'fg_bez', 'Bundesland', 'year', 'month', 'weekday_hours', 'saturday_hours', 'weekday_hours_extra', 'saturday_hours_extra'], dtype='object')
2025-11-06 06:59:23,703 - conversion_functions.py - DEBUG - 168 - business hours file: (3996, 9) Index(['fg_sg', 'fg_bez', 'Bundesland', 'year', 'month', 'weekday_hours', 'saturday_hours', 'weekday_hours_extra', 'saturday_hours_extra'], dtype='object')
2025-11-06 06:59:23,802 - conversion_functions.py - DEBUG - 168 - business hours file: (4662, 10) Index(['fg_sg', 'fg_bez', 'Bundesland', 'year', 'month', 'weekday_hours', 'saturday_hours', 'weekday_hours_extra', 'saturday_hours_extra', 'season'], dtype='object')