Abstract

In this paper, we propose a novel support vector regression (SVR) approach for time series analysis. An efficient forward feature selection strategy has been designed for dealing with high-frequency time series with multiple seasonal periods. Inspired by the literature on feature selection for support vector classification, we designed a technique for assessing the contribution of additional covariates to the SVR solution, including them in a forward fashion. Our strategy extends the reasoning behind Auto-ARIMA, a well-known approach for automatic model specification for traditional time series analysis, to kernel machines. Experiments on well-known high-frequency datasets demonstrate the virtues of the proposed method in terms of predictive performance, confirming the virtues of an automatic model specification strategy and the use of nonlinear predictors in time series forecasting. Our empirical analysis focus on the energy load forecasting task, which is arguably the most popular application for high-frequency, multi-seasonal time series forecasting.