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Time series forecasting with the WARIMAX-GARCH method

Corrêa, J. M.; Neto, A. C.; Teixeira Júnior, L. A.; Franco, E. M. C. and Faria Jr, A. E. (2016). Time series forecasting with the WARIMAX-GARCH method. Neurocomputing, 216 pp. 805–815.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.neucom.2016.08.046
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Abstract

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting.

Item Type: Journal Item
Copyright Holders: 2016 Elsevier B.V.
ISSN: 0925-2312
Keywords: wavelet decomposition; exogenous variable; ARIMA-GARCH model; forecasting
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 47282
Depositing User: Álvaro Faria
Date Deposited: 12 Sep 2016 13:17
Last Modified: 25 May 2019 20:33
URI: http://oro.open.ac.uk/id/eprint/47282
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