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- W2022755802 abstract "To reduce the complexity of training an least squares support vector machine (LSSVM), a nearest neighbors method was proposed to perform Multi-step time series prediction. By selecting dataset with smallest Euclidean distance and similar changing trend for each testing instance, a reduced training dataset was defined. Experiments on chaotic datasets were conducted to compare the prediction performance with traditional interactive single-step methods. The results demonstrate that the proposed method outperforms the single-step methods. The ability of Multi-step prediction is promising even when the noises were added." @default.
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- W2022755802 date "2012-06-01" @default.
- W2022755802 modified "2023-09-24" @default.
- W2022755802 title "K-nearest neighbor LS-SVM method for multi-step prediction of chaotic time series" @default.
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- W2022755802 doi "https://doi.org/10.1109/eeesym.2012.6258677" @default.
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