get_prediction(..., dynamic=False) Prediction of each point will use all historic observations prior to it http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.mlemodel.MLEResults.get_prediction.html#statsmodels.regression.recursive_ls.MLEResults.get_prediction http://pandas.pydata....
我们首先将预测值与时间序列的实际值进行比较,这将有助于我们了解我们的预测的准确性。get_prediction()和conf_int()属性允许我们获得时间序列预测的值和相关的置信区间。 pred = results.get_prediction(start=pd.to_datetime('1998-01-01'), dynamic=False)#预测值 pred_ci = pred.conf_int()#置信区间 上述...
return None def prediction(model_fit, train, test): # 预测 return model_fit.predict(start=len(train), end=len(train) + len(test) - 1, dynamic=False) # 要输入预测开始和结束的位置 if __name__ == '__main__': train, test = getData(path) model_fit = getModel(train[:, 1]) pri...
How to train the SARIMA model Now we are ready to build the SARIMA model. We can use theSARIMAXclass provided by thestatsmodelslibrary. We fit the model and get the prediction through theget_prediction()function. We can retrieve also the confidence intervals through theconf_int()function. fro...
Training the long and short-term memory neural network with the residual sample set to get a trained network for residual prediction. Finally, to sum the two parts predicted values up to obtain the predicted value of the wind speed series. In order to test the prediction effect of this ...
Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model...
3. Make Prediction Once fit, the model can be used to make a forecast. A forecast can be made by calling the forecast() or the predict() functions on the SARIMAXResults object returned from calling fit. The forecast() function takes a single parameter that specifies the number of out of...
In order to get better performance on prediction, we constructed SARIMA model based on statistics and LSTM model based on machine learning, and compared their prediction results. The MAE, MSE, RMSE and MAPE of SARIMA and LSTM were 1.137, 2.750, 1.658, 0.051 and 0.654, 0.869, 0.932 and ...
Within the training and test sets, a prediction was made once per calendar day. In practice however, this was too computatinoally intensive for the SARIMA, and prophet models given the resources of the project (2 weeks). The intervals were therefore reduced. Project needs and core tasks data...
recurrent neural network (GRU) is used to fit the seasonal change for the seasonal component, and the back-propagation neural network (BPNN) is used to fit the nonlinear and stochastic change for the residual component, and the combination reconstruction is used to get...