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Compound Autoregressive Network for Prediction of Multivariate Time Series

Author

Listed:
  • Yuting Bai
  • Xuebo Jin
  • Xiaoyi Wang
  • Tingli Su
  • Jianlei Kong
  • Yutian Lu

Abstract

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

Suggested Citation

  • Yuting Bai & Xuebo Jin & Xiaoyi Wang & Tingli Su & Jianlei Kong & Yutian Lu, 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series," Complexity, Hindawi, vol. 2019, pages 1-11, September.
    • Handle: RePEc:hin:complx:9107167
    DOI: 10.1155/2019/9107167
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    References listed on IDEAS

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    1. Roman Matkovskyy & Taoufik Bouraoui, 2019. "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 433-446, June.
    2. Hong Yao & Wei Zhuang & Yu Qian & Bisheng Xia & Yang Yang & Xin Qian, 2016. "Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    3. Doucoure, Boubacar & Agbossou, Kodjo & Cardenas, Alben, 2016. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data," Renewable Energy, Elsevier, vol. 92(C), pages 202-211.
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    Cited by:

    1. Xue-Bo Jin & Nian-Xiang Yang & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction," Mathematics, MDPI, vol. 8(2), pages 1-17, February.
    2. Yi Yang & Yuting Bai & Xiaoyi Wang & Li Wang & Xuebo Jin & Qian Sun, 2020. "Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    3. Xue-Bo Jin & Xing-Hong Yu & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System," Sustainability, MDPI, vol. 12(4), pages 1-18, February.

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