eprintid: 4659
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/46/59
datestamp: 2023-11-09 16:16:21
lastmod: 2023-11-09 16:16:21
status_changed: 2023-11-09 15:59:02
type: article
metadata_visibility: show
creators_name: Abdulkadir, S.J.
creators_name: Yong, S.-P.
creators_name: Marimuthu, M.
creators_name: Lai, F.-W.
title: Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
ispublished: pub
keywords: Filtration; Finance; Forecasting, Auto-regressive; Chaotic time series; Ensemble Kalman Filter; Ensemble modeling; Financial forecasting; Forecasting modeling; Long-term dependencies; Unscented Kalman Filter, Kalman filters
note: cited By 20; Conference of 2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014 ; Conference Date: 10 December 2014 Through 12 December 2014; Conference Code:111739
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. © Springer International Publishing Switzerland 2014.
date: 2014
publisher: Springer Verlag
official_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
id_number: 10.1007/978-3-319-13817-6₈
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 8891
pagerange: 72-81
refereed: TRUE
isbn: 9783319138169
issn: 03029743
citation:   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