Abstract
This paper proposes a novel algorithm called Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with a kernel filter and a modified Drift Detector Mechanism (Meta-RKOS-ELM\(_\mathrm{ALD}\)-DDM). The algorithm aims to tackle a well-known concept drift problem in time series prediction by utilising the modified concept drift detector mechanism. Moreover, the new meta-cognitive learning strategy is employed to solve parameter dependency and reduce learning time. The experimental results show that the proposed method can achieve better performance than the conventional algorithm in a set of financial datasets.
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Acknowledgement
This research was supported by the Frontier Research Grant from the University of Malaya (Project No. FG003-17AFR), the International Collaboration Fund from MESTECC (Project No. CF001-2019), ONRG NICOP grant (Project No: IF017-2018) from Office of Naval Research Global, UK, and the Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang.
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Liu, Z., Loo, C.K., Pasupa, K. (2019). Real-Time Financial Data Prediction Using Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_41
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