Improving SSA Predictions by Inverse Distance Weighting

Abstract

This paper proposes a method of utilizing spatial information to improve predictions in one dimensional time series analysis using singular spectrum analysis (SSA). It employs inverse distance weighting for spatial averaging and subsequently multivariate singular spectrum analysis (MSSA) for enhanced forecasts. The technique is exemplified on a data set for rainfall recordings from Upper Austria.