mode=mode, target_component=predict_component) print(multi_time_series.head()) features = ["load"] targets = ["load"] time_format = '%Y-%m-%d %H:%M:%S' if freq == 'd': time_format = '%Y-%m-%d' train_inputs, valid_inputs, test_inputs, y_scaler = split_train_validation_test( multi_time_series, valid_start_time=valid_start_dt, test_start_time=test_start_dt, time_step_lag=time_step_lag, horizon=HORIZON, features=features, target=targets, freq=freq, time_format=time_format) X_train = train_inputs['X'] y_train = train_inputs['target_load'] X_valid = valid_inputs['X'] y_valid = valid_inputs['target_load'] # input_x = train_inputs['X'] print("train_X shape", X_train.shape) print("valid_X shape", X_valid.shape) # print("target shape", y_train.shape) # print("training size:", len(train_inputs['X']), 'validation', len(valid_inputs['X']), 'test size:', len(test_inputs['X']) )
output_dir = '/home/long/TTU-SOURCES/self-boosted-ts/output/temperature' multi_time_series = load_data_full(data_dir, datasource='electricity', imfs_count=imfs_count) print(multi_time_series.head()) print("count data rows=", multi_time_series.count) valid_start_dt = '2013-05-26 14:15:00' test_start_dt = '2014-03-14 19:15:00' train_inputs, valid_inputs, test_inputs, y_scaler = split_train_validation_test( multi_time_series, valid_start_time=valid_start_dt, test_start_time=test_start_dt, time_step_lag=time_step_lag, horizon=HORIZON, features=["load"], target=['load']) X_train = train_inputs['X'] y_train = train_inputs['target_load'] X_valid = valid_inputs['X'] y_valid = valid_inputs['target_load'] # input_x = train_inputs['X'] print("train_X shape", X_train.shape) print("valid_X shape", X_valid.shape) # print("target shape", y_train.shape) # print("training size:", len(train_inputs['X']), 'validation', len(valid_inputs['X']), 'test size:', len(test_inputs['X']) )
multi_time_series = load_data_full(data_dir, datasource='exchange-rate', imfs_count=imfs_count, freq='d') print(multi_time_series.head()) valid_start_dt = '2002-06-18' test_start_dt = '2006-08-13' features = ["load", "imf9", "imf10", "imf8", "imf7"] train_inputs, valid_inputs, test_inputs, y_scaler = split_train_validation_test( multi_time_series, valid_start_time=valid_start_dt, test_start_time=test_start_dt, time_step_lag=time_step_lag, horizon=HORIZON, features=features, target=features, time_format='%Y-%m-%d', freq='d') # ['imf6', 'imf5', 'imf4', 'imf3', 'imf2', 'imf0', 'imf1'] aux_features = [ "load", "imf6", "imf5", 'imf4', 'imf3', 'imf2', 'imf0', 'imf1' ] # for i in range(imfs_count): # l = 'imf' + str(i) # if l not in features: # aux_features.append(l) aux_inputs, aux_valid_inputs, aux_test_inputs, aux_y_scaler = split_train_validation_test(