A two-sided CP-decomposed structure for tensor autoregression regresses response features on covariate features, matching Tucker-level interpretability at CP-level parameter counts, plus a low-rank-plus-sparse variant with non-asymptotic error bounds.
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An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
A two-sided CP-decomposed structure for tensor autoregression regresses response features on covariate features, matching Tucker-level interpretability at CP-level parameter counts, plus a low-rank-plus-sparse variant with non-asymptotic error bounds.