10.6084/m9.figshare.1266504.v2
Jaime Carpio
Jaime
Carpio
Jesús Juan
Jesús
Juan
Damián López
Damián
López
Multivariate Exponential Smoothing and Dynamic Factor Model Applied to Hourly Electricity Price Analysis
Taylor & Francis Group
2015
multivariate process
Multivariate Exponential Smoothing
Hourly Electricity Price Analysis Thanks
em
parameter
factor analysis
likelihood estimation
forecast time series
electricity price predictions
computer implementation
Nord Pool markets
Dynamic Factor Model Applied
multivariate version
model
2015-05-07 21:01:49
Dataset
https://tandf.figshare.com/articles/dataset/Multivariate_Exponential_Smoothing_and_Dynamic_Factor_Model_Applied_to_Hourly_Electricity_Price_Analysis/1266504
<div><p>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 expectation-maximization (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 article has supplementary material online.</p></div>