On Multivariate Singular Spectrum Analysis and its Variants

• URL: http://arxiv.org/abs/2006.13448v5
• Date: Sun, 19 Jun 2022 04:16:30 GMT
• Title: On Multivariate Singular Spectrum Analysis and its Variants
• Authors: Anish Agarwal, Abdullah Alomar, Devavrat Shah
• Abstract summary: We introduce and analyze a variant of multivariate singular analysis (mSSA), a popular time series method. We establish prediction mean-squared-error for both imputation and out-of-sample forecasting effectively as $1 / sqrtmin(N, T )T$. On benchmark datasets, our variant of mSSA performs competitively with state-of-the-art neural-network time series methods.
• Score: 23.517864567789353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $1 / \sqrt{\min(N, T )T}$. This is an improvement over: (i) $1 /\sqrt{T}$ 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 $T$.

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