Descripción
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Thanks to its very simple recursive computing scheme, exponential smoothing has become a popular technique to forecast time series. In this work we show the advantages of its multivariate version and present some properties of the model which allows us to perform a dynamic factor analysis. This analysis leads to a simple methodology to reduce the number of parameters (useful when the dimension of observations is large) via a linear transformation that decomposes the multivariate process into independent univariate exponential smoothing processes, characterized by a single smoothing parameter that goes from zero (white noise process) to one (random walk process). A computer implementation of the EM algorithm has been built for the maximum likelihood estimation of the models. The practicality of the method is demonstrated by its application to hourly electricity price predictions in some day-ahead markets, such as Omel, Powernext, and Nord Pool markets, whose forecasts are given as examples. This paper has supplementary material online. | |
Internacional
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Si |
JCR del ISI
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Si |
Título de la revista
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Technometrics |
ISSN
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0040-1706 |
Factor de impacto JCR
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1,424 |
Información de impacto
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Datos JCR del año 2012 |
Volumen
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-- |
DOI
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10.1080/00401706.2013.860920 |
Número de revista
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-- |
Desde la página
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Hasta la página
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Mes
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SIN MES |
Ranking
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26/117 STATISTIC and PROBABILITY |