Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting

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, c...

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Main Authors: Abdulkadir, S.J., Yong, S.-P., Marimuthu, M., Lai, F.-W.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.31853 /
Published: Springer Verlag 2014
Online Access: 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
http://eprints.utp.edu.my/31853/
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spelling utp-eprints.318532022-03-29T03:38:37Z Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting Abdulkadir, S.J. Yong, S.-P. Marimuthu, M. Lai, F.-W. 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. Springer Verlag 2014 Article NonPeerReviewed 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 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. http://eprints.utp.edu.my/31853/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
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.
format Article
author Abdulkadir, S.J.
Yong, S.-P.
Marimuthu, M.
Lai, F.-W.
spellingShingle Abdulkadir, S.J.
Yong, S.-P.
Marimuthu, M.
Lai, F.-W.
Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
author_sort Abdulkadir, S.J.
title Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
title_short Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
title_full Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
title_fullStr Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
title_full_unstemmed Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
title_sort hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
publisher Springer Verlag
publishDate 2014
url 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
http://eprints.utp.edu.my/31853/
_version_ 1741197648273080320
score 11.62408

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