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- W2954287553 abstract "This paper presents a novel methodology for time series prediction. It is based on Extreme Learning Machines and an adaptive ensemble techniques. It is tested successfully on the CIF 2016 competition datasets which are composed of 72 time series in total. Among those, 48 time series are artificial with each having 108 training data points and 12 testing points. So for each artificial time series, there are 120 values, which is more than that of the rest 24 real time series." @default.
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- W2954287553 date "2019-06-30" @default.
- W2954287553 modified "2023-10-05" @default.
- W2954287553 title "A Novel ELM Ensemble for Time Series Prediction" @default.
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- W2954287553 doi "https://doi.org/10.1007/978-3-030-23307-5_31" @default.
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