Integrating kernel principal component analysis with least squares support vector machines for time series forecasting problems
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摘要: 探討了最小二乘支持向量機時間序列預測的方法,提出了用核主成分分析提取主元,然后用最小二乘支持向量機進行預測．通過實驗表明,這種方法得到的效果優于沒有特征提取的預測．同時與主成分分析提取特征相比,用核主成分分析效果更好．Abstract: This paper discusses least squares support vector machines (LSSVM) in the time series forecasting problem. Kernel principal component analysis (KPCA) is proposed to calculate principal component. Least squares support vector machines are applied to predict time series. Experimental results show that the performance of LSSVM with feature extraction using KPCA is much better than that without feature extraction. In comparison with PCA, there is also superior Derforrnance in KPCA.
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