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Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

Author

Listed:
  • Thierry Moudiki

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Frédéric Planchet

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Areski Cousin

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, IRMA - Institut de Recherche Mathématique Avancée - UNISTRA - Université de Strasbourg - CNRS - Centre National de la Recherche Scientifique)

Abstract

We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models ( Pankratz 2012 ), with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL) neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Post-Print hal-02055155, HAL.
    • Handle: RePEc:hal:journl:hal-02055155
    DOI: 10.3390/risks6010022
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    References listed on IDEAS

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    1. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
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    5. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    6. Christoph Bergmeir & Rob J Hyndman & Bonsoo Koo, 2015. "A Note on the Validity of Cross-Validation for Evaluating Time Series Prediction," Monash Econometrics and Business Statistics Working Papers 10/15, Monash University, Department of Econometrics and Business Statistics.
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