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Subject: search.filters.subject.Recurrent algorithm

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    A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction
    (2021-03-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    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<sup>−3</sup> on the Mackey-Glass data set and 1.49 × 10<sup>−4</sup> on the Lorenz data sets.
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    Meta-cognitive recurrent kernel online sequential extreme learning machine with kernel adaptive filter for concept drift handling
    (2020-02-01)
    Liu, Zongying
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    ;
    Seera, Manjeevan
    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-ELM<inf>ALD</inf>). 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-ELM<inf>ALD</inf> with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms.