An Ensembel Model for Modelling Chaotic Behaviour of Bursa Malaysia Time Series Data
Financial data is characterized as non-linearity, chaotic in nature and volatility thus making the process of forecasting cumber- some, hence a successful forecasting model must be able to capture long- term dependencies from chaotic data. In this study, an ensemble model, called UKF-NARX, consi...
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Online Access: | http://eprints.utp.edu.my/11579/1/Vivian-iconip14_Nov2014.pdf http://eprints.utp.edu.my/11579/ |
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| Summary: | Financial data is characterized as non-linearity, chaotic in
nature and volatility thus making the process of forecasting cumber-
some, hence a successful forecasting model must be able to capture long-
term dependencies from chaotic data. In this study, an ensemble model,
called UKF-NARX, consists of unscented kalman �lter and parallel non-
linear autoregressive network with exogenous input trained with bayesian
regulation algorithm is modelled for chaotic �nancial forecasting. The
proposed ensemble model is compared with the conventional non-linear
autoregressive network and �nancial static forecasting model employed
by �nancial analysts when applying in multi-step-ahead forecasting. Ex-
perimental results on Burssa Malaysia KLCI show that the proposed
ensemble model outperforms the other two commonly used models. |
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