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Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting

Said, Jadid Abdulkadir and Yong, S.P. and Maran, Marimuthu and Lai, Fong Woon (2014) Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting. In: Mining Intelligence and Knowledge Exploration. Springer, London, pp. 72-81. ISBN 978-3-319-13816-9

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Abstract

Financial data is characterized as non-linear, chaotic in nature and volatile thus making the process of forecasting cumbersome. Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. The proposed hybrid model is compared with commonly used Elman-NARX and static forecasting model employed by financial analysts. Experimental results on Bursa Malaysia KLCI data show that the proposed hybrid model outperforms the other two commonly used models.

Item Type:Book Section
Academic Subject One:Artificial intelligence financial Forecasting
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:11716
Deposited By: Dr. Fong-Woon Lai
Deposited On:07 Oct 2016 01:42
Last Modified:07 Oct 2016 01:42

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