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-ELMALD-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.
CITATION STYLE
Liu, Z., Loo, C. K., & Pasupa, K. (2019). Real-time financial data prediction using meta-cognitive recurrent kernel online sequential extreme learning machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11955 LNCS, pp. 488–498). Springer. https://doi.org/10.1007/978-3-030-36718-3_41
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