An Adaptive Approach to Time‐series Forecasting

ABSTRACT This paper extends the applicability of the Carbone‐Longini adaptive estimation procedure (AEP) to time‐series forecasting. Comparisons with adaptive filtering, the Box‐Jenkins methodology, and multiple regression analysis as it applies to time‐series analysis are provided. Specific time‐series data examined by Box and Jenkins and Box and Tiao constitute the basis for these comparisons. The analysis of the results indicate the robustness and performance superiority of the simple distributive‐lag forecast model coupled with the concept of adaptively “tracking” rather than “fitting” historical data.