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A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction
Author(s)
Date Issued
March 1, 2021
Type
Article
Abstract
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 ×10−3 on the Mackey-Glass data set and 1.49 × 10−4 on the Lorenz data sets.
Citation
Sustainable Cities and Society, 66, 2021