TY  - JOUR
Y1  - 2014///
UR  - 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
A1  - Abdulkadir, S.J.
A1  - Yong, S.-P.
A1  - Marimuthu, M.
A1  - Lai, F.-W.
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
VL  - 8891
N2  - 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.
KW  - Filtration; Finance; Forecasting
KW  -  Auto-regressive; Chaotic time series; Ensemble Kalman Filter; Ensemble modeling; Financial forecasting; Forecasting modeling; Long-term dependencies; Unscented Kalman Filter
KW  -  Kalman filters
ID  - scholars4659
SN  - 03029743
PB  - Springer Verlag
EP  - 81
AV  - none
N1  - 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
SP  - 72
TI  - Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
ER  -