Using ARIMA model and neuro-fuzzy approach to forecast the climatic temperature in Mosul-Iraq

The accuracy of temperature forecasting in maximum and minimum cases is important to control the environmental effects. In this study, integrated autoregressive and moving average (ARIMA) model is used to forecast climatic temperature variable in maximum and minimum cases in Mosul, Iraq as traditional method. Neuro-Fuzzy (NF) is also proposed as modern approach to improve the forecasting results. The results in this study reflect outperforming in forecasting for NF approach comparing to ARIMA model. In conclusion, NF approach can be used for more accuracy to forecast climatic temperature datasets in maximum and minimum cases.