Abstract
Time series model selection has been widely studied in recent years. It is of importance to select the best model among candidate models proposed for a series in terms of explaining the procedure that governs the series and providing the most accurate forecast for the future observations. In this study, it is aimed to create an algorithm for order selection in Box-Jenkins models that combines penalized natural logarithm of mutual information among the original series and predictions coming from each candidate. The penalization is achieved by subtracting the number of parameters in each candidate and empirical information the data provide.Simulation studies under various scenarios and applications on real data sets imply that our algorithm offers a promising and satisfactory alternative to its counterparts.