Precipitation Forecasting Method Using Box-Jenkins Time Series Model (Case Study: Ghaemshahr Station)

Box-Jenkins prediction model is one of the most famous time series models and is important in predicting different geographic phenomena. In Box-Jenkins methodology, time series models are in fact autoregressive integrated moving average models that are known as ARIMA models in statistics. Various models such as simple and multivariate regression, autoregressive, moving average, seasonal models and even unknown models can be derived from ARIMA models. In this research, while expressing the precipitation forecasting method using the Box-Jenkins time series model practically and by fitting this model on the monthly precipitation data of Ghaemshahr synoptic station, which contains statistics of 50 years, the best model for forecasting precipitation in this station was selected, which was from the type of SARIMA (1.0.1) (0.1. 1) model.