Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

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A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on Cropped Oversized Button Up an online sequential extreme learning machine (OS-ELM) in this paper.In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process.The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly.Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm.In addition, a real-world mechanical system identification problem is ACU considered to test the feasibility and efficacy of the AWOS-ELM algorithm.