On Multivariate Singular Spectrum Analysis and its Variants

We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series. Under a spatio-temporal factor model we introduce, given N time series and T observations per time series, we establish prediction mean-squared-error for both imputation and out-of-sample forecasting effectively scale as 1 / √(min(N, T )T). This is an improvement over: (i) 1 /√(T) error scaling of SSA, the restriction of mSSA to a univariate time series; (ii) 1/min(N, T) error scaling for matrix estimation methods which do not exploit temporal structure in the data. The spatio-temporal model we introduce includes any finite sum and products of: harmonics, polynomials, differentiable periodic functions, and Holder continuous functions. Our out-of-sample forecasting result could be of independent interest for online learning under a spatio-temporal factor model. Empirically, on benchmark datasets, our variant of mSSA performs competitively with state-of-the-art neural-network time series methods (e.g. DeepAR, LSTM) and significantly outperforms classical methods such as vector autoregression (VAR). Finally, we propose extensions of mSSA: (i) a variant to estimate time-varying variance of a time series; (ii) a tensor variant which has better sample complexity for certain regimes of N and T.