计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 157-162.doi: 10.11896/j.issn.1002-137X.2019.05.024
李文海1,2, 程佳宇2, 谢晨阳2
LI Wen-hai1,2, CHENG Jia-yu2, XIE Chen-yang2
摘要： 针对大样本下周期性时间序列预测的问题,文中给出了一种基于DTW距离的相似样本度量方法。首先,给出周期性时间序列预测问题的定义,并基于支持向量回归方法分析大量噪声点对预测误差的影响。然后,通过对时间序列周期分段来构建相似性度量,在给定预测样本容量下确定给定预测条件的相似样本子集。同时,基于误差调谐函数对SVM的核函数进行调整,以进一步提升预测精度。最后,基于常用的周期性时间序列,在预测精度上将所提方法与已有算法进行实验比较,并分析该模型的参数敏感性。实验结果验证了所提方法的有效性。
中图分类号:
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