This paper proposes a meta-cognitive recurrent multi-step-prediction model called Meta-cognitive Recurrent Recursive Kernel Online Sequential Extreme Learning Machine with a new modified Drift Detector Mechanism (Meta-RRKOS-ELM-DDM). This model combines the strengths of Recurrent Kernel Online Sequential Extreme Learning Machine (RKOS-ELM) with the recursive kernel method and a new meta-cognitive learning strategy. We apply Drift Detector Mechanism to solve concept drift problem. Recursive kernel method successfully replaces the normal kernel method in RKOS-ELM and generates a fixed reservoir with optimised information. The new meta-cognitive learning strategy can reduce the computational complexity. The experimental results show that Meta-RRKOS-ELM-DDM has a superior prediction ability in different predicting horizons than the others.
CITATION STYLE
Liu, Z., Loo, C. K., & Pasupa, K. (2018). Handling concept drift in time-series data: Meta-cognitive recurrent recursive-kernel OS-ELM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11306 LNCS, pp. 3–13). Springer Verlag. https://doi.org/10.1007/978-3-030-04224-0_1
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