Hybridization of 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.