TY - JOUR
T1 - Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling
AU - Liu, Zongying
AU - Loo, Chu Kiong
AU - Pasupa, Kitsuchart
AU - Seera, Manjeevan
N1 - Funding Information:
The authors express their gratitude to the Frontier Research Grant from the University of Malaya, Malaysia (Project No. FG003-17AFR ), the International Collaboration Fund from MESTECC, Malaysia (Project No. IF0318M1006 ), 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.
Funding Information:
The authors express their gratitude to the Frontier Research Grant from the University of Malaya, Malaysia (Project No. FG003-17AFR), the International Collaboration Fund from MESTECC, Malaysia (Project No. IF0318M1006), 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.
Publisher Copyright:
© 2019 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELMALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELMALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
AB - This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELMALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELMALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.
KW - Concept drift
KW - Kernel method
KW - Multi-step prediction
KW - Recurrent algorithm
KW - Time series prediction
UR - https://www.scopus.com/pages/publications/85075734700
U2 - 10.1016/j.engappai.2019.103327
DO - 10.1016/j.engappai.2019.103327
M3 - Article
AN - SCOPUS:85075734700
SN - 0952-1976
VL - 88
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 103327
ER -