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Machine Learning

, Volume 108, Issue 8–9, pp 1421–1441 | Cite as

Temporal pattern attention for multivariate time series forecasting

  • Shun-Yao ShihEmail author
  • Fan-Keng Sun
  • Hung-yi Lee
Article
  • 1.1k Downloads
Part of the following topical collections:
  1. Special Issue of the ECML PKDD 2019 Journal Track

Abstract

Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in almost all of cases. Our source code is available at https://github.com/gantheory/TPA-LSTM.

Keywords

Multivariate time series Attention mechanism Recurrent neural network Convolutional neural network Polyphonic music generation 

Notes

Acknowledgements

This work was financially supported by the Ministry of Science and Technology of Taiwan.

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Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Taiwan UniversityTaipeiTaiwan

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