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: | , , , |
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| Format: | Article |
| Published: |
Springer Verlag
2014
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| 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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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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. |
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