Python one_step_esvr_multi_seed 예제들

예제 #1

파일: esvr_one_step.py 프로젝트: zjy8006/MonthlyRunoffForecastByAutoReg

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(

예제 #2

    #     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

예제 #3

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',

예제 #4

파일: esvr_one_step_one_month.py 프로젝트: YX577/MonthlyRunoffForecastByAutoReg

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,

예제 #5

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,
    )