import sys import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) sys.path.append(root_path) from tools.models import one_step_esvr, one_step_esvr_multi_seed from Huaxian_modwt.projects.variables import variables if __name__ == '__main__': for lead_time in [1, 3, 5, 7]: one_step_esvr_multi_seed( root_path=root_path, station='Huaxian', decomposer='modwt', predict_pattern='single_hybrid_' + str(lead_time) + '_ahead_lag12_mi_ts0.1', # forecast or forecast or forecast_with_pca_mle or forecast_with_pca_mle n_calls=100, wavelet_level='db1-4', ) # one_step_esvr_multi_seed( # root_path=root_path, # station='Huaxian', # decomposer='modwt', # predict_pattern='single_hybrid_1_ahead',# forecast or forecast or forecast_with_pca_mle or forecast_with_pca_mle # n_calls=100, # wavelet_level=wavelet_level, # ) # for lead_time in [1,3,5,7,9]: # one_step_esvr_multi_seed(
# n_calls=100, # ) # for leading_time in [1,3,5,7,9]: # one_step_esvr_multi_seed( # root_path=root_path, # station='Zhangjiashan', # decomposer='ssa', # predict_pattern='one_step_'+str(leading_time)+'_ahead_forecast_pacf',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle # n_calls=100, # ) for leading_time in [1, 3, 5, 7, 9]: one_step_esvr_multi_seed( root_path=root_path, station='Zhangjiashan', decomposer='ssa', predict_pattern='one_step_' + str(leading_time) + '_ahead_forecast_pcc_local', # hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) # one_step_esvr_multi_seed( # root_path=root_path, # station='Zhangjiashan', # decomposer='ssa', # predict_pattern='one_step_1_ahead_forecast_pacf_pca18',#+str(i),# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle # n_calls=100, # ) # one_step_esvr_multi_seed( # root_path=root_path, # station='Zhangjiashan', # decomposer='ssa', # predict_pattern='one_step_1_ahead_forecast_pacf_pcamle',#+str(i),# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle
import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) import sys sys.path.append(root_path) from tools.models import one_step_esvr,one_step_esvr_multi_seed if __name__ == '__main__': one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='modwt', predict_pattern='one_step_1_ahead_forecast_pacf',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) for leading_time in [3,5,7,9]: one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='modwt', predict_pattern='one_step_'+str(leading_time)+'_ahead_forecast_pearson0.2',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100,) for leading_time in [3,5,7,9]: one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='modwt',
import sys import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) sys.path.append(root_path) from tools.models import one_step_esvr, one_step_esvr_multi_seed from Huaxian_vmd.projects.variables import variables if __name__ == '__main__': one_step_esvr_multi_seed( root_path=root_path, station='Huaxian', decomposer='vmd', predict_pattern= 'one_step_1_ahead_forecast_pacf_traindev_test', # hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) for leading_time in [1, 3, 5, 7, 9]: one_step_esvr_multi_seed( root_path=root_path, station='Huaxian', decomposer='vmd', predict_pattern='one_step_' + str(leading_time) + '_ahead_forecast_pacf', # hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) for leading_time in [3, 5, 7, 9]: one_step_esvr_multi_seed( root_path=root_path,
import sys import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) sys.path.append(root_path) from tools.models import one_step_esvr, one_step_esvr_multi_seed from Xianyang_dwt.projects.variables import variables if __name__ == '__main__': one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='dwt', predict_pattern='one_step_1_ahead_forecast_pacf_traindev_test',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='dwt', predict_pattern='one_step_1_ahead_forecast_pacf_train_val',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, ) one_step_esvr_multi_seed( root_path=root_path, station='Xianyang', decomposer='dwt', predict_pattern='one_step_1_ahead_forecast_pacf_traindev_append',# hindcast or forecast or hindcast_with_pca_mle or forecast_with_pca_mle n_calls=100, )