A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems |
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| Affiliation: | 1. Higher Institute of Applied Sciences and Technology, 4003 Sousse, Tunisia;2. National School of Computer Sciences, 2010 Manouba, Tunisia;1. Department of Electrical Engineering, Sarvestan Branch, Islamic Azad University, Sarvestan, Iran;2. School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;3. American University of Sharjah, Sharjah, United Arab Emirates;1. Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, Niš, Serbia;2. Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, Niš, Serbia;1. Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia;2. Odette School of Business, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada;1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China;2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada;3. School of Information, Liaoning University, Shenyang 110036, China;1. Grup de Recerca en Sistemes Intel·ligents, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;2. Grup de Recerca en Internet Technologies & Storage, Ramon Llull University, Quatre Camins 2, 08022 Barcelona, Spain;3. Departamento de Ingeniería Matemática e Informática, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain |
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| Abstract: | The paper proposes a parsimonious nonlinear framework for modeling bivariate stochastic processes. The method is a vector autoregressive-like approach equipped with a wavelet-based feedforward neural network, allowing practitioners dealing with extremely random two-dimensional information to make predictions and plan their future more and more precisely. Artificial Neural Networks (ANN) are recognized as powerful computing devices and universal approximators that proved valuable for a wide range of univariate time series problems. We expand their coverage to handle nonlinear bivariate data. Wavelet techniques are used to strengthen the procedure, since they allow to break up processes information into a finite number of sub-signals, and subsequently extract microscopic patterns in both time and frequency fields. The proposed model can be very valuable especially when modeling nonlinear econophysical systems with high extent of volatility. |
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| Keywords: | Bivariate processes Forecasting Wavelet coefficients Feedforward neural networks Nonlinear autoregressive models Econophysics |
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