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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| Main Authors: | , , , |
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| Format: | Citation Index Journal |
| Published: |
2014
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| Subjects: | |
| Online Access: | 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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| 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. |
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