Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks

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Data in time series format, such as biological signals from medical sensors or machine signals from sensors in industrial environments are rich sources of information that can give crucial insights on the present and future condition of a person or machine. The task of predicting future values of time series has been initially approached with simple machine learning methods, and lately with deep learning. Two models that have shown good performance in this task are the temporal convolutional network and the attention module. However, despite the promising results of deep learning methods, their black-box nature makes them unsuitable for real-world applications where the predictions need to be explainable in order to be trusted. In this paper we propose an architecture comprised of a temporal convolutional network with an attention mechanism that makes predictions while presenting the timesteps of the input that were most influential for future outputs. We apply it on two datasets and we show that we gain interpretability without degrading the accuracy compared to the original temporal convolutional models. We then go one step further and we combine our configuration with various machine learning methods on top, creating a pipeline that achieves interpretability both across timesteps and input features. We use it to forecast a different variable from one of the above datasets and we study how the accuracy is affected compared to the original black-box approach.

Original languageEnglish
Title of host publication2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Number of pages8
ISBN (Electronic)9781728125473
Publication statusPublished - 5 Jan 2021
Event2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia
Duration: 1 Dec 20204 Dec 2020


Conference2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
CityVirtual, Canberra


  • forecasting
  • time series
  • deep learning
  • Interpretability
  • Temporal attention
  • convolutional neural network


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