A Hybrid SARIMA‐LSTM Model for Air Temperature Forecasting. Issue 2 (22nd December 2022)

• Record Type: Journal Article
• Authors: Li, Guoqiang; Yang, Ning
• Is Part Of: Advanced theory and simulations. Volume 6:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-22
• Subjects: air temperature -- forecasting -- hybrid model -- LSTM -- SARIMA -- time series; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202200502
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: In order to improve the prediction accuracy of air temperature forecasting, a temperature prediction model based on the hybrid SARIMA (seasonal autoregressive integrated moving average)‐LSTM (long short‐term memory) model is constructed. First, this method decomposes the temperature series into three series of trend, seasonal, and residual through seasonal‐trend decomposition procedure based on Loess decomposition method. It establishes SARIMA to predict the trend and seasonal series and extracts the linear information contained in the time series to the maximum extent. Then, the LSTM model is used to fit the residual series and the hidden nonlinear information is further extracted. Finally, the prediction results of two parts are added in series to obtain the prediction result of the final hybrid model. Three indexes, namely, root mean square error, mean absolute error, and mean absolute percentage error are evaluated to calculate the prediction accuracy about single models including ARIMA, SARIMA, and LSTM and the hybrid models ARIMA‐LSTM and SARIMA‐LSTM. Also the Kupiec index is used to show tail performance. The empirical results show that the SARIMA‐LSTM combination model is more accurate than the single prediction methods and other combination model. Its accuracy increases by 10.0–27.7%. Abstract : This method decomposes the temperature series based on Loess decomposition method. It establishes seasonal autoregressive integrated moving average to predict the trend and seasonal series. Then, the long short‐term memory model is used to fit the residual series. Finally, the prediction results of two parts are added in series to obtain the prediction result of the final hybrid model.