Track: Mathematical Modeling and Heuristics

Abstract

Autoregressive integrated moving average (ARIMA) models have been proven successful in application and simple in comprehension and consequently, they have been widely applied to different fields in forecasting. The order of an ARIMA model is determined subjectively based on the judgment of the experts where, the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots for a given time series are used to determine the potential orders of the model. In this paper, a new heuristic algorithm is proposed for determining the order of ARIMA models. The proposed method determines the order of the ARIMA models, objectively, based on the Mean Squared Error (MSE), Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (BIC). In this regard, the order of the models is determined objectively and as a result, the forecasting results would be more accurate. The performance of the proposed method is evaluated based on a real-world dataset of global temperature anomaly where, the results show that the proposed method performs accurately and efficiently in determining the order of ARIMA models.