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,
)
