计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 71-78.doi: 10.11896/jsjkx.200500044
黄铭1,2, 孙林夫1,2, 任春华1,2, 吴奇石1,3
HUANG Ming1,2, SUN Lin-fu1,2, REN Chun-hua1,2 , WU Qi-shi1,3
摘要： 近年来,随着数据挖掘和机器学习的兴起,基于时间序列分析方法的研究愈加丰富。作为机器学习的经典方法,KNN(K-Nearest Neighbor)因其简单、准确度高等特性被广泛应用于时间序列分析的各个领域。然而,使用原始的KNN回归方法预测时间序列具有一定的局限性,直接使用欧氏距离作为相似度度量方法的预测效果并不理想,无法适应具有整体趋势变化的时间序列的预测场景。文中提出一种拟合时间序列趋势的KNN算法TSTF-KNN(Time Series Trend Fitting KNN)算法,该方法通过对每个时刻的特征序列进行归一化处理,改进了KNN相似度度量的效果,使之可以更有效地搜索相似的特征序列。由于序列预测前进行了归一化,文中通过为预测结果添加误差项来还原序列特征,使之可以有效地预测结果。为了验证方法的有效性,从kaggle公开数据集中选取了4个数据集,并通过对这4个数据集分别进行预处理获得5个时间序列以供实验。通过使用TSTF-KNN、KNN、单层LSTM(Long Short-Term Memory)神经网络和ANN(Artificial Neural Network)在处理后的5个时间序列上进行预测实验,分析预测结果,并对比均方误差(Mean Square Error,MSE),验证了该方法的有效性。实验结果表明,该方法能有效提高KNN回归方法对时间序列预测的准确度和稳定性,使之可以更好地适应具有整体趋势变化的时间序列的预测场景。
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