Explainable Tensorized Neural Ordinary Differential Equations for Arbitrary-Step Time Series Prediction

Penglei Gao, Xi Yang, Rui Zhang, Kaizhu Huang*, John Y. Goulermas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In this work, we propose a continuous neural network architecture, referred to as Explainable Tensorized Neural - Ordinary Differential Equations (ETN-ODE) network for multi-step time series prediction at arbitrary time points. Unlike existing approaches which mainly handle univariate time series for multi-step prediction, or multivariate time series for single-step predictions, ETN-ODE is capable of handling multivariate time series with arbitrary-step predictions. An additional benefit is its tandem attention mechanism, with respect to temporal and variable attention, which enable it to greatly facilitate data interpretability. Specifically, the proposed model combines an explainable tensorized gated recurrent unit with ordinary differential equations, with the derivatives of the latent states parameterized through a neural network. We quantitatively and qualitatively demonstrate the effectiveness and interpretability of ETN-ODE on one arbitrary-step prediction task and five standard multi-step prediction tasks. Extensive experiments show that the proposed method achieves very accurate predictions at arbitrary time points while attaining very competitive performance against the baseline methods in standard multi-step time series prediction.

Original languageEnglish
Pages (from-to)5837-5850
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number6
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • ODEs
  • Time series prediction
  • neural networks
  • tensorized GRU

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