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Real-time financial data prediction using meta-cognitive recurrent kernel online sequential extreme learning machine
Author(s)
Date Issued
January 1, 2019
Type
Conference Paper
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-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
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11955 LNCS, 488-498, 2019