Identifying secondary series for stepwise common singular spectrum analysis

Abstract

Common singular spectrum analysis is a technique which can be used to forecast a primary time series by using the information from a secondary series. Not all secondary series, however, provide useful information. A first contribution in this paper is to point out the properties which a secondary series should have in order to improve the forecast accuracy of the primary series. The second contribution is a proposal which can be used to select a secondary series from several candidate series. Empirical studies suggest that the proposal performs well.