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

1
ISFA, Laboratoire SAF, Université Claude Bernard Lyon I, 69100 Villeurbanne, France
2
Laboratoire IRMA, Université de Strasbourg, 67081 Strasbourg, France
*
Author to whom correspondence should be addressed.
Risks 2018, 6(1), 22; https://doi.org/10.3390/risks6010022
Received: 3 January 2018 / Revised: 16 February 2018 / Accepted: 26 February 2018 / Published: 12 March 2018
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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. View Full-Text
Keywords: forecasting; multivariate time series; dynamic regression; neural networks forecasting; multivariate time series; dynamic regression; neural networks
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Moudiki, T.; Planchet, F.; Cousin, A. Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks. Risks 2018, 6, 22.

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