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Understanding exponential smoothing via kernel regression
Author(s) -
Gijbels I.,
Pope A.,
Wand M. P.
Publication year - 1999
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00161
Subject(s) - exponential smoothing , smoothing , nonparametric regression , kernel smoother , kernel (algebra) , kernel regression , residual , computer science , exponential function , regression , realization (probability) , nonparametric statistics , series (stratigraphy) , mathematics , econometrics , kernel method , algorithm , statistics , artificial intelligence , mathematical analysis , combinatorics , radial basis function kernel , support vector machine , paleontology , biology
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.