Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula

TL;DR

This paper enhances Singular Spectrum Analysis (SSA) for time series forecasting by introducing a non-linear recurrent formula, demonstrating improved accuracy in economic data with structural breaks over traditional SSA methods.

Contribution

It proposes a novel non-linear recurrent formula for SSA, improving forecasting performance in the presence of structural breaks in economic time series.

Findings

SSA with the general recurrent formula outperforms basic SSA and bootstrap methods in RMSE.

No significant difference in predicting the direction of change among methods.

The proposed SSA model is recommended for series with structural breaks.

Abstract

This study extends and evaluates the forecasting performance of the Singular Spectrum Analysis (SSA) technique using a general non-linear form for the re- current formula. In this study, we consider 24 series measuring the monthly seasonally adjusted industrial production of important sectors of the German, French and UK economies. This is tested by comparing the performance of the new proposed model with basic SSA and the SSA bootstrap forecasting, especially when there is evidence of structural breaks in both in-sample and out-of-sample periods. According to root mean-square error (RMSE), SSA using the general recursive formula outperforms both the SSA and the bootstrap forecasting at horizons of up to a year. We found no significant difference in predicting the direction of change between these methods. Therefore, it is suggested that the SSA model with the general recurrent formula should be chosen by users in the case of structural breaks in the series.