A new approach for the vector forecast algorithm in singular spectrum analysis

摘要

The window length, L, is the first parameter that must be specified in Singular Spectrum Analysis (SSA) for time series analysis. A large window length has a potential to produce a good model fit, but it is unlikely to produce a parsimonious forecasting model. In this paper, we propose a new parsimonious vector forecasting model which uses an optimal m () coefficients for forecasting, instead of the L - 1 coefficients used in the standard vector forecasting method. This model enables SSA users to consider two different values for the window length: one for reconstruction and another for forecasting. The proposed and standard methods are compared methodologically and also implemented and tested on daily observations of six stocks: AAPL, AMZN, EBAY, IBM, INTC, MSFT, between Jan 1 2000 and Dec 31 2015, each including 4025 observations. It was found that the method proposed in this paper provides major improvements regarding forecast accuracy.