只有在启用AR-Net的情况下,才会支持Lagged Regressor NeuralProphet通过调用add_lagged_regressor函数并给出必要的设置,将Lagged Regressor用于NeuralProphet对象中。 假如有一个名为A的滞后回归项,通过add_lagged_regressor注册它到NeuralProphet m = m.add_lagged_regressor(names=’A’) Future Regressors 未来回归器是...
只有在启用AR-Net的情况下,才会支持Lagged Regressor NeuralProphet通过调用add_lagged_regressor函数并给出必要的设置,将Lagged Regressor用于NeuralProphet对象中。 假如有一个名为A的滞后回归项,通过add_lagged_regressor注册它到NeuralProphet m=m.add_lagged_...
17. NeuralProphet通过add_lagged_regressor注册协变量 df=df_ercot m=NeuralProphet( n_forecasts=24, n_lags=24, learning_rate=0.01, ) m=m.add_lagged_regressor(names=regions) m.highlight_nth_step_ahead_of_each_forecast(24) metrics=m.fit(df,freq="H") 1. 2. 3. 4. 5. 6. 7. 8. 9...
config_lagged_regressors, config_regressors, config_events, config_seasonality, local_run_despite_global, df=df_i, normalize=normalize, config_lagged_regressors=config_lagged_regressors, config_regressors=config_regressors, config_events=config_events, config_seasonality=config_seasonality, local_run_des...
add_lagged_regressor(names=regions) m.highlight_nth_step_ahead_of_each_forecast(24) metrics = m.fit(df, freq="H") 1 2 3 4 5 6 7 8 9 10 11预测forecast = m.predict(df) # fig = m.plot(forecast) fig1 = m.plot(forecast[-365*24:]) fig2 = m.plot(forecast[-7*24:]) #...
m=m.add_lagged_regressor(names='A') 1. https://neuralprophet.com/html/lagged-regressors.html 特性3:添加特殊事件 在预测问题中需要考虑重复发生的特殊事件,可以以加法格式和乘法格式添加: m=NeuralProphet( n_forecasts=10, yearly_seasonality=False, ...
m=m.add_lagged_regressor("I",normalize="standardize") metrics=m.fit(df_train,freq='H',validation_df=df_test,progress='plot') m=m.highlight_nth_step_ahead_of_each_forecast(1) forecast=m.predict(df_test) fig=m.plot(forecast)