Arima Model Optimal Selection for Time Series Forecasting

Abstract

Abstract A fast-and-flexible method of ARIMA model optimal selection is suggested for univariate time series forecasting. The method allows obtaining as-highly-accurate-as-possible forecasts automatically. It is based on effectively finding lags by the autocorrelation function of a detrended time series, where the best-fitting polynomial trend is subtracted from the time series. The forecasting quality criteria are the root-mean-square error (RMSE) and the maximum absolute error (MaxAE) allowing to register information about the average inaccuracy and worst outlier. Thus, the ARIMA model optimal selection is performed by simultaneously minimizing RMSE and Max-AE, whereupon the minimum defines the best model. Otherwise, if the minimum does not exist, a combination of minimal-RMSE and minimal-MaxAE ARIMA models is used.