  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 $T$.
