Alireza Ataei

Singular Spectrum Analysis (SSA) is an increasingly popular time series filtering and forecasting technique. Owing to its widespread applications in a variety of fields, there is a growing interest towards improving its forecasting capabilities. The proposed Recurrent SSA­R approach is referred to as Weighted SSA­R (W:SSA­R), and we propose using a weighting algorithm for weighting the coefficients of the Linear Recurrent Relation (LRR). The performance of forecasts from the W:SSA­R approach are compared with forecasts from the established SSA­R. We exploit real data and various simulated time series for the comparison, so as to provide the reader with more conclusive findings. Our results confirm that the W:SSA­R can provide comparatively more accurate forecasts and is indeed a viable solution for improving forecasts by SSA.