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INFORMS 2020
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A method for high-dimensional probabilistic multivariate time series forecasting

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Abstract

Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simplifying distribution assumptions or abandons modeling cross-series correlations. A promising line of work exploits scalable matrix factorization for latent-space forecasting, but is limited to linear relationships, is not probabilistic, and not trainable end-to-end. We introduce a novel probabilistic multivariate forecasting method addressing these shortcomings, and demonstrate improved performance on a variety of multivariate datasets.

Date

07 Sep 2020

Publication

INFORMS 2020

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