@article{scholars4659,
       publisher = {Springer Verlag},
            note = {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},
          volume = {8891},
         journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
           pages = {72--81},
             doi = {10.1007/978-3-319-13817-6{$_8$}},
           title = {Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting},
            year = {2014},
          author = {Abdulkadir, S. J. and Yong, S.-P. and Marimuthu, M. and Lai, F.-W.},
            isbn = {9783319138169},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915766968&doi=10.1007\%2f978-3-319-13817-6\%5f8&partnerID=40&md5=3e4e1eb3fa629c3ffe8728d89e724cfd},
            issn = {03029743},
        abstract = {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. {\^A}{\copyright} Springer International Publishing Switzerland 2014.},
        keywords = {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}
}