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(
