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Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification

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Abstract

Time series classification (TSC) has been around for recent decades as a significant research problem for industry practitioners as well as academic researchers. Due to the rapid increase in temporal data in a wide range of disciplines, an incredible amount of algorithms have been proposed. This paper proposes robust approaches based on state-of-the-art techniques, bidirectional long short-term memory (BiLSTM), fully convolutional network (FCN), and attention mechanism. A BiLSTM considers both forward and backward dependencies, and FCN is proven to be good at feature extraction as a TSC baseline. Therefore, we augment BiLSTM and FCN in a hybrid deep learning architecture, BiLSTM-FCN. Moreover, we similarly explore the use of the attention mechanism to check its efficiency on BiLSTM-FCN and propose another model ABiLSTM-FCN. We validate the performance on 85 datasets from the University of California Riverside (UCR) univariate time series archive. The proposed models are evaluated in terms of classification testing error and f1-score and also provide performance comparison with various existing state-of-the-art techniques. The experimental results show that our proposed models perform comprehensively better than the existing state-of-the-art methods and baselines.

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Acknowledgments

This paper was partially supported by NSFC grant U1509216, U1866602, 61602129, and Microsoft Research Asia.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Mehak Khan, Hongzhi Wang, Aya Elfatyany & Sajida Karim

  2. School of Computer Science and Technology, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China

    Adnan Riaz

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  1. Mehak Khan
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  2. Hongzhi Wang
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  3. Adnan Riaz
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  4. Aya Elfatyany
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  5. Sajida Karim
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Correspondence to Mehak Khan.

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Khan, M., Wang, H., Riaz, A. et al. Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification. J Supercomput 77, 7021–7045 (2021). https://doi.org/10.1007/s11227-020-03560-z

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