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Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

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Advances in Machine Learning/Deep Learning-based Technologies

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 23))

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Abstract

We present a framework for multivariate nonlinear time series forecasting that utilizes phase space image representations and deep learning. Recurrence plots (RP) are a phase space visualization tool used for the analysis of dynamical systems. This approach takes advantage of recurrence plots that are used as input image representations for a class of deep learning algorithms called convolutional neural networks. We show that by leveraging recurrence plots with optimal embedding parameters, appropriate representations of underlying dynamics are obtained by the proposed autoregressive deep learning model to produce forecasts.

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Authors and Affiliations

  1. University of the Philippines Manila, Manila, Philippines

    Sun Arthur A. Ojeda & Geoffrey A. Solano

  2. DOST-Advanced Science and Technology Institute, Quezon City, Philippines

    Elmer C. Peramo

Authors
  1. Sun Arthur A. Ojeda
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  2. Elmer C. Peramo
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  3. Geoffrey A. Solano
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Corresponding author

Correspondence to Geoffrey A. Solano .

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Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Ojeda, S.A.A., Peramo, E.C., Solano, G.A. (2022). Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C. (eds) Advances in Machine Learning/Deep Learning-based Technologies. Learning and Analytics in Intelligent Systems, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-76794-5_9

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