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

Said , Jadid Abdulkadir and Suet Peng, Yong 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
Departments / MOR / COE: Departments > Management & Humanities
Depositing User: Dr. Fong-Woon Lai
Date Deposited: 07 Oct 2016 01:42
Last Modified: 07 Oct 2016 01:42
URI: http://scholars.utp.edu.my/id/eprint/11715

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