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Online DCA for Times Series Forecasting Using Artificial Neural Network

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Optimization of Complex Systems: Theory, Models, Algorithms and Applications (WCGO 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 991))

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Abstract

In this work, we study the online time series forecasting problem using artificial neural network. To solve this problem, different online DCAs (Difference of Convex functions Algorithms) are investigated. We also give comparison with online gradient descent—the online version of one of the most popular optimization algorithm in the collection of neural network problems. Numerical experiments on some benchmark time series datasets validate the efficiency of the proposed methods.

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  1. 1.

    http://archive.ics.uci.edu/ml.

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Author information

Authors and Affiliations

  1. LGIPM, University of Lorraine, Metz, France

    Viet Anh Nguyen & Hoai An Le Thi

Authors
  1. Viet Anh Nguyen
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  2. Hoai An Le Thi
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Corresponding author

Correspondence to Viet Anh Nguyen .

Editor information

Editors and Affiliations

  1. Computer science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department, LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences (INSA)-Rouen Normadie, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

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Nguyen, V.A., Le Thi, H.A. (2020). Online DCA for Times Series Forecasting Using Artificial Neural Network. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_33

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