Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting

Abdulkadir, S.J. and Yong, S.-P. and Marimuthu, M. and Lai, F.-W. (2014) Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8891. pp. 72-81.

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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. © Springer International Publishing Switzerland 2014.

Item Type: Article
Impact Factor: cited By 16
Uncontrolled 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
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 29 Mar 2022 03:38
Last Modified: 29 Mar 2022 03:38
URI: http://scholars.utp.edu.my/id/eprint/31853

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