Hybridization on 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,...
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my.utp.eprints.120672017-03-20T00:31:48Z Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting Abdulkadir, Said Jadid Yong, Suet-Peng Marimuthu, Maran Lai, Fong Woon HG Finance 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. 2014 Citation Index Journal PeerReviewed application/pdf http://eprints.utp.edu.my/12067/1/Hybridization%20on%20Ensemble%20Kalman%20Filter%20and%20Non-Linear%20Auto-Regressive%20Neural%20Network%20for%20Financial%20Forecasting.pdf Abdulkadir, Said Jadid and Yong, Suet-Peng and Marimuthu, Maran and Lai, Fong Woon (2014) Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting. [Citation Index Journal] http://eprints.utp.edu.my/12067/ |
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HG Finance Abdulkadir, Said Jadid Yong, Suet-Peng Marimuthu, Maran Lai, Fong Woon Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
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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. |
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Citation Index Journal |
author |
Abdulkadir, Said Jadid Yong, Suet-Peng Marimuthu, Maran Lai, Fong Woon |
author_facet |
Abdulkadir, Said Jadid Yong, Suet-Peng Marimuthu, Maran Lai, Fong Woon |
author_sort |
Abdulkadir, Said Jadid |
title |
Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
title_short |
Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
title_full |
Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
title_fullStr |
Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
title_full_unstemmed |
Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting |
title_sort |
hybridization on ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting |
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2014 |
url |
http://eprints.utp.edu.my/12067/1/Hybridization%20on%20Ensemble%20Kalman%20Filter%20and%20Non-Linear%20Auto-Regressive%20Neural%20Network%20for%20Financial%20Forecasting.pdf http://eprints.utp.edu.my/12067/ |
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