Identification of time series based on methods of singular spectrum analysis and modeleteka

Singular spectrum analysis (SSA) is relatively new method for analysis of non-stationary time series. In this paper we propose to use variative modeling, based on joint use of SSA and method of modeleteka (models warehouse), for obtaining of analytical model of time series that combines adequacy, compactness, and interpretability. First, time series are decomposed into components using SSA; significant components are selected using formal indicators. Second, each significant component is identified according to the purpose of identification with simple and interpretable model from preformed modeleteka. The result is final model of time series in additive or additive-multiplicative form. Applicability of the method is illustrated on synthetic data.