%0 Journal Article
%@ 03029743
%A Abdulkadir, S.J.
%A Yong, S.-P.
%A Marimuthu, M.
%A Lai, F.-W.
%D 2014
%F scholars:4659
%I Springer Verlag
%J Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
%K Filtration; Finance; Forecasting, Auto-regressive; Chaotic time series; Ensemble Kalman Filter; Ensemble modeling; Financial forecasting; Forecasting modeling; Long-term dependencies; Unscented Kalman Filter, Kalman filters
%P 72-81
%R 10.1007/978-3-319-13817-6₈
%T Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
%U https://khub.utp.edu.my/scholars/4659/
%V 8891
%X 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.
%Z cited By 20; Conference of 2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014 ; Conference Date: 10 December 2014 Through 12 December 2014; Conference Code:111739