A Comparison of Some Information Criteria to Select a Weather Forecast Model

Abstract

The purpose of using the criteria for selecting models is to determine an appropriate model that leads to estimates that we can use in making future predictions. In this study,  we presented a number of information criteria that help to choose the best model in the time series, such as  Akaike information criterion (AIC),  Bayesian information criterion ( BIC), Akaike corrected information criterion (AIC_C) and Pham information criterion (PIC). We  aim to obtain a appropriate  model for a time series used to predict the lowest temperatures in Erbil governorate for the next three years depending on the  Box-Jenkins methodology for the purpose of building time series models.

The best model was chosen from among the estimated models based on the value of the aforementioned statistical criteria. The model was used to predict the lowest temperatures for the next three years in Erbil governorate, as the predictive values ​​were consistent with the real values ​​of temperature ranges.