relation: https://khub.utp.edu.my/scholars/4659/
title: Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
creator: Abdulkadir, S.J.
creator: Yong, S.-P.
creator: Marimuthu, M.
creator: Lai, F.-W.
description: 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. © Springer International Publishing Switzerland 2014.
publisher: Springer Verlag
date: 2014
type: Article
type: PeerReviewed
identifier:   Abdulkadir, S.J. and Yong, S.-P. and Marimuthu, M. and Lai, F.-W.  (2014) Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8891.  pp. 72-81.  ISSN 03029743     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915766968&doi=10.1007%2f978-3-319-13817-6_8&partnerID=40&md5=3e4e1eb3fa629c3ffe8728d89e724cfd
relation: 10.1007/978-3-319-13817-6₈
identifier: 10.1007/978-3-319-13817-6₈