Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction
This paper proposes a novel recurrent multi-step-ahead prediction model called recurrent kernel extreme reservoir machine (RKERM) with quantum particle swarm optimization (QPSO). This model combines the strengths of recurrent kernel extreme learning machine (RKELM) and modified reservoir computing t...
| Main Authors: | , , , |
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| 格式: | 文件 |
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Institute of Electrical and Electronics Engineers
2018
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| _version_ | 1825721684328448000 |
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| author | Liu, Zongying Loo, Chu Kiong Masuyama, Naoki Pasupa, Kitsuchart |
| author_facet | Liu, Zongying Loo, Chu Kiong Masuyama, Naoki Pasupa, Kitsuchart |
| author_sort | Liu, Zongying |
| collection | UM |
| description | This paper proposes a novel recurrent multi-step-ahead prediction model called recurrent kernel extreme reservoir machine (RKERM) with quantum particle swarm optimization (QPSO). This model combines the strengths of recurrent kernel extreme learning machine (RKELM) and modified reservoir computing to overcome the limitations of prediction horizon with increased prediction accuracy based on reservoir computing theory. Furthermore, QPSO is used to optimize the parameters of kernel method and leaking rate of reservoir computing in the RKERM. In the experiment, we apply two synthetic benchmark data sets and five real-world time series data sets, including Malaysia palm oil price, ozone concentration in Toronto, sunspots, Standard Poor's 500, and water level at Phra Chulachomklao Fort in Thailand to evaluate the echo state network, recurrent support vector regression, recurrent extreme learning machine, RKELM, and RKERM. The experimental results show that the RKERM with QPSO has superior abilities in the different predicting horizons than others. |
| first_indexed | 2024-03-06T05:54:06Z |
| format | Article |
| id | um.eprints-21416 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:54:06Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-214162019-05-30T03:40:51Z http://eprints.um.edu.my/21416/ Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction Liu, Zongying Loo, Chu Kiong Masuyama, Naoki Pasupa, Kitsuchart QA75 Electronic computers. Computer science This paper proposes a novel recurrent multi-step-ahead prediction model called recurrent kernel extreme reservoir machine (RKERM) with quantum particle swarm optimization (QPSO). This model combines the strengths of recurrent kernel extreme learning machine (RKELM) and modified reservoir computing to overcome the limitations of prediction horizon with increased prediction accuracy based on reservoir computing theory. Furthermore, QPSO is used to optimize the parameters of kernel method and leaking rate of reservoir computing in the RKERM. In the experiment, we apply two synthetic benchmark data sets and five real-world time series data sets, including Malaysia palm oil price, ozone concentration in Toronto, sunspots, Standard Poor's 500, and water level at Phra Chulachomklao Fort in Thailand to evaluate the echo state network, recurrent support vector regression, recurrent extreme learning machine, RKELM, and RKERM. The experimental results show that the RKERM with QPSO has superior abilities in the different predicting horizons than others. Institute of Electrical and Electronics Engineers 2018 Article PeerReviewed Liu, Zongying and Loo, Chu Kiong and Masuyama, Naoki and Pasupa, Kitsuchart (2018) Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction. IEEE Access, 6. pp. 19583-19596. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2018.2823336 <https://doi.org/10.1109/ACCESS.2018.2823336>. https://doi.org/10.1109/ACCESS.2018.2823336 doi:10.1109/ACCESS.2018.2823336 |
| spellingShingle | QA75 Electronic computers. Computer science Liu, Zongying Loo, Chu Kiong Masuyama, Naoki Pasupa, Kitsuchart Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title | Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title_full | Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title_fullStr | Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title_full_unstemmed | Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title_short | Recurrent Kernel Extreme Reservoir Machine for Time Series Prediction |
| title_sort | recurrent kernel extreme reservoir machine for time series prediction |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT liuzongying recurrentkernelextremereservoirmachinefortimeseriesprediction AT loochukiong recurrentkernelextremereservoirmachinefortimeseriesprediction AT masuyamanaoki recurrentkernelextremereservoirmachinefortimeseriesprediction AT pasupakitsuchart recurrentkernelextremereservoirmachinefortimeseriesprediction |