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

Thierry Moudiki (), Frédéric Planchet () and Areski Cousin ()
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Thierry Moudiki: ISFA, Laboratoire SAF, Université Claude Bernard Lyon I, 69100 Villeurbanne, France
Frédéric Planchet: ISFA, Laboratoire SAF, Université Claude Bernard Lyon I, 69100 Villeurbanne, France
Areski Cousin: Laboratoire IRMA, Université de Strasbourg, 67081 Strasbourg, France

Risks, 2018, vol. 6, issue 1, 1-20

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.

Keywords: forecasting; multivariate time series; dynamic regression; neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2018
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