Using the State Space Model based on ARIMA Model for Air Temperature Forecasting.
Abstract
The high accuracy of forecasts with the temperature data is very important to control environmental damages such as desertification and water resources drought as well as it is important to control the uses of renewable energy and clean energy. Using the multiplicative seasonal integrated auto-regressive and moving average (SARIMA) model for forecasting with uncertainty problem in the modeling process especially with nonlinear data such as minimum temperatures will make the forecasting results become low in quality because ARIMA is a linear model. Improving the minimum temperature forecasting quality is the main aim for this study by using more suitable methods for modeling the data with the problem of uncertainty. In this study, the minimum temperature data for Mosul and Baghdad will be used as a case of study. The state space (SS) will be used based on the ARIMA model which can be called the hybrid ARIMA-SS model which will be used to solve the uncertainty problem caused by the non-linearity of temperature data. Therefore the forecasting results may be not accurate. Also, the climate data often suffers from heterogeneity, especially in non-tropical regions, due to the high difference between the hot and cold seasons of these data. Time stratified (TS) will be used to solve the problem of data heterogeneity. In the ARIMA-SS hybrid method ARIMA is used only for the purpose of specifying the input of the SS model. In this study, the SS model was used as a statistical method for estimating and forecasting the state space. The SS method is to combine observations with current forecasts values by using weights that reduce biases and errors. The ARIMA-SS hybrid model has been used to deal with uncertainty and improve the minimum temperature forecasting by handling it well. The performance of the ARIMA model and the ARIMA-SS hybrid model will be compared to determine which of them will perform with more accurate forecasts .The results showed that the ARIMA-SS hybrid model outperformed the ARIMA model and produced more accurate forecasts. Therefore, it is possible to conclude that ARIMA-SS hybrid model can be used to result better forecasting accuracy for the minimum temperature compared to the forecasting performance of the traditional ARIMA model.
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