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CDA-LSTM: an evolutionary convolution-based dual-attention LSTM for univariate time series prediction

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Abstract

Univariate time series forecasting is still an important but challenging task. Considering the wide application of temporal data, adaptive predictors are needed to study historical behavior and forecast future state in various scenarios. In this paper, inspired by human attention mechanism and decomposition and reconstruction framework, we proposed a convolution-based dual-stage attention (CDA) architecture combined with Long Short-Term Memory networks (LSTM) for univariate time series forecasting. Specifically, we first use the decomposition algorithm to generate derived variables from target series. Input variables are then fed into the CDA-LSTM machine for further forecasting. In the Encoder–Decoder phase, for the first stage, attention operation is combined with the LSTM acting as an encoder, which could adaptively learn the relevant derived series to the target. In the second stage, the temporal attention mechanism is integrated with decoder aiming to automatically select the relevant encoder hidden states across all time steps. A convolution phase is concatenated parallelly to the Encoder–Decoder phase to reuse the historical information of the target and extract the mutation features. The experimental results demonstrate the proposed method could be adopted as expert systems for forecasting in multiple scenarios, and the superiority is verified by comparing with twelve baseline models on ten datasets. The practicability of different decomposition algorithms and convolution architectures is also discussed by extensive experiment. Overall, our work carries a significant value not merely in adaptive modeling of deep learning in time series issues, but also in the field of univariate data processing and prediction.

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Availability of data and code

Access to all the datasets involved in this paper has been provided on the relevant pages. For the code of this article, please contact Xiaoquan Chu (chuxq@cau.edu.cn).

Notes

  1. Available at http://www.sidc.be/silso/home.

  2. Available at https://fred.stlouisfed.org/series/STLFSI2.

  3. Available at https://fred.stlouisfed.org/series/DEXCHUS.

  4. Available at https://fred.stlouisfed.org/series/DEXUSEU.

  5. Available at http://www.eia.doe.gov.

  6. Available at https://www.aemo.com.au/

  7. Available at http://www.3w3n.com.

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Acknowledgements

This study was supported by the Chinese Agricultural Research System (CARS-29) and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture, PR China.

Funding

This study was funded by the Chinese Agricultural Research System (CARS-29) and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture, PR China.

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Authors and Affiliations

  1. Collage of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Xiaoquan Chu, Haibin Jin, Yue Li, Jianying Feng & Weisong Mu

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  1. Xiaoquan Chu
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  2. Haibin Jin
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Contributions

XC contributed to conceptualization, methodology, and writing manuscript. HJ and YL performed data curation and writing—review and editing. JF performed supervision, writing—review and editing. WM contributed to funding acquisition, supervision, and writing—review and editing.

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Correspondence to Weisong Mu.

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Appendix

Appendix

See Figs. 

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Forecasting results of SUNSPOT dataset

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Forecasting results of STLFSI dataset

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Forecasting results of DEXCHUS dataset

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Forecasting results of DEXUSEU dataset

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Forecasting results of WTI dataset

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Forecasting results of NSW dataset

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Forecasting results of TAS dataset

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Forecasting results of QLD dataset

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Forecasting results of VIC dataset

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Forecasting results of CTGP dataset

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Chu, X., Jin, H., Li, Y. et al. CDA-LSTM: an evolutionary convolution-based dual-attention LSTM for univariate time series prediction. Neural Comput & Applic 33, 16113–16137 (2021). https://doi.org/10.1007/s00521-021-06212-2

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  • DOI: https://doi.org/10.1007/s00521-021-06212-2

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