import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) from variables import multi_step_lags import sys sys.path.append(root_path) from models import multi_step_esvr if __name__ == '__main__': for i in range(1, 4): multi_step_esvr( root_path=root_path, station='Zhangjiashan', decomposer='dwt', predict_pattern='multi_step_1_month_forecast', llags_dict=variables['lags_dict'], model_id=i, n_calls=100, )
import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) # root_path = os.path.abspath(os.path.join(root_path,os.path.pardir)) # For run in CMD # root_path = os.path.abspath(os.path.join(root_path,os.path.pardir)) print("root_path:{}".format(root_path)) from variables import multi_step_lags import sys sys.path.append(root_path) from models import multi_step_esvr if __name__ == '__main__': for i in range(1, 10): multi_step_esvr( root_path=root_path, station='Huaxian', decomposer='eemd', predict_pattern='multi_step_1_month_forecast', llags_dict=variables['lags_dict'], model_id=i, #1:9 n_calls=100, )
import matplotlib.pyplot as plt import os root_path = os.path.dirname(os.path.abspath('__file__')) from variables import multi_step_lags import sys sys.path.append(root_path) from models import multi_step_esvr if __name__ == '__main__': for i in range(1, 13): multi_step_esvr( root_path=root_path, station='Xianyang', decomposer='ssa', predict_pattern='multi_step_1_month_forecast', llags_dict=variables['lags_dict'], model_id=i, #1:12 n_calls=100, )