预测结果如下表所示。 pred_dynamic = results.get_prediction(start=pd.to_datetime('2014-01-1'), dynamic=True, full_results=True) pred_dynamic_ci = pred_dynamic.conf_int() mte_forecast = pred_dynamic.predicted_mean mte_orginal = mte['2014-01-1':] mte_pred_concat = pd.concat([mte_org...
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....
#动态预测 pred_dynamic = results.get_prediction(start=pd.to_datetime('2014-01-1'), dynamic=True, full_results=True) pred_dynamic_ci = pred_dynamic.conf_int() ts_forecast = pred_dynamic.predicted_mean ts_orginal =ts['2014-01-01':] ts_forecast.plot(color='blue', label='Predict') ts...
pred=results.get_prediction(start=pd.to_datetime('2022-01-01'),dynamic=False)pred_ci=pred.conf_int()ax=data['2010':].plot(label='observed')pred.predicted_mean.plot(ax=ax,label='One-step ahead Forecast',alpha=.7,figsize=(14,7)) 1. 2. 3. 4. 5. 总结 通过以上步骤,您可以成功实现...
我们已经获得了我们时间序列的模型,现在可以用来产生预测。 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=Fal...
平稳模型(stationary model):描述平稳观测序列的模型。 趋势平稳(trend stationary):不显示趋势的时间序列。 季节性平稳(seasonal stationary):不表现出季节性的时间序列。 严格平稳(strictly stationary):平稳过程的数学定义,特别指观测值的联合分布不受时移的影响。
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 ...
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 the final prediction value...
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 ...
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...