A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction

Liu, Zongying and Loo, Chu Kiong and Pasupa, Kitsuchart (2021) A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction. Sustainable Cities and Society, 66. ISSN 2210-6707, DOI https://doi.org/10.1016/j.scs.2020.102613.

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With the development of industry and technology, the development of the environment and cities has drawn lots of attention. Time series prediction plays a vital role in protecting the environment and improving the level of intelligence and technology in cities, for example prediction of air pollution, water levels, palm oil prices, financial data and grid security. We describe a new algorithm, ``Error-output Recurrent Two-layer Extreme Learning Machine'' or ERT-ELM: it applied a new recurrent technique, that not only removed the restriction of the prediction horizon problem, but it also used a mean squared error of the current step to update the output weights for the next step. This technique avoided error accumulation in the original recurrent algorithm for multi-step time series prediction. Moreover, the new two-layer structure network improved forecasting compared to conventional single-layer or two-layer ELM models. Quantum behaved Particle Swarm Optimization was used to find suitable ERT-ELM parameters. The ability of our model was assessed on ten data sets-two artificial and eight real-world data sets and performed significantly better than the baselines. Especially for the synthetic data sets, in 1-18 prediction periods, our model achieved mean square errors of 2.64 x 10(-3) on the Mackey-Glass data set and 1.49 x 10(-4) on the Lorenz data sets.

Item Type: Article
Funders: King Mongkuts Institute of Technology Ladkrabang (KREF206307), University of Malaya under the UM Partnership Grant (RK0122019)
Uncontrolled Keywords: Two-layer network; Multi-step time-series prediction; Recurrent algorithm; Error-output recurrent
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Ms Zaharah Ramly
Date Deposited: 25 May 2022 06:08
Last Modified: 25 May 2022 06:08
URI: http://eprints.um.edu.my/id/eprint/27150

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