Identification of seasonal arima models using a filtering method

Abstract

This paper proposes an identification method of ARIMA models for seasonal time series using an intermediary model and a filtering method. This method is found to be useful when conventional methods, such as using sample ACF and PACF, fail to reveal a clear-cut model. This filtering identification method is also found to be particularly effective when a seasonal time series is subjected to calendar variations, moving-holiday effects, and interventions.

Keywords:

• Model identification
• seasonal time series
• ARIMA models
• filtering
• intermediary models
• calendar variation
• intervention
• transfer function models