Understanding exponential smoothing via kernel regression

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1999 · Volume 61 · Issue 1

Abstract

Exponential smoothing is the most common model-free means of forecasting a future realization of a time series. It requires the specification of a smoothing factor which is usually chosen from the data to minimize the average squared residual of previous one-step-ahead forecasts. In this paper we show that exponential smoothing can be put into a nonparametric regression framework and gain some interesting insights into its performance through this interpretation. We also use theoretical developments from the kernel regression field to derive, for the first time, asymptotic properties of exponential smoothing forecasters.

Keywords

Bandwidth selection, Cross-validation, Dependent errors regression, Kernel smoothing, Limiting distribution, Local polynomial

Enable JavaScript for citation tools and additional paper interactions.