Publication Date

10-2001

Series Number

Preprint # - 2001-15

Abstract

Statistical models (e.g., ARIMA models) have commonly been used in time series data analysis and forecasting. Typically, one model is selected based on a selection criterion (e.g., AIC), hypothesis testing, and/or graphical inspection. The selected model is then used to forecast future values. However, model selection is often unstable and may cause an unnecessarily high variability in the final estimation/prediction. In this work, we propose the use of an algorithm, AFTER, to convexly combine the models for a better performance of prediction. The weights are sequentially updated after each additional observation. Simulations and real data examples are used to compare the performance of our approach with model selection methods. The results show an advantage of combining by AFTER over selection in terms of forecasting accuracy at several settings.

Comments

This preprint was published as Hui Zou and Yuhong Yang, "Combining Time Series Models for Forecasting", International Journal of Forecasting (2004): 69-84, doi: 10.1016/S0169-2070(03)00004-9.

Language

en

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