基于加權(quán)動態(tài)時間彎曲的多元時間序列相似性匹配方法
發(fā)布時間：2018-10-30 20:14
【摘要】：針對常用方法忽略變量相關(guān)性和局部形狀特性問題,提出基于加權(quán)動態(tài)時間彎曲的多元時間序列相似性匹配方法(CPCA-SWDTW).首先,在原加權(quán)動態(tài)時間彎曲算法基礎(chǔ)上,引入形態(tài)因子,提出基于形態(tài)特征的加權(quán)動態(tài)時間彎曲算法(SWDTW).然后,提取多元時間序列的主成分作為模式表示,消除變量間的相關(guān)性,同時將方差貢獻率作為相應(yīng)主成分的權(quán)重.在此基礎(chǔ)上,運用SWDTW,度量多元時間序列間的相似度.最后,通過相似性搜索實驗表明,CPCA-SWDTW具有較好的準確性和魯棒性.敏感性分析說明CPCA-SWDTW在一定程度上受到權(quán)重函數(shù)參數(shù)的影響.
[Abstract]:Aiming at the problem of ignoring variable correlation and local shape characteristics in common methods, a multi-variable time series similarity matching method (CPCA-SWDTW) based on weighted dynamic time bending is proposed. First of all, based on the original weighted dynamic time bending algorithm and the introduction of morphological factor, a weighted dynamic time bending algorithm (SWDTW). Based on morphological features is proposed. Then, the principal component of the multivariate time series is extracted as the pattern representation, and the correlation between variables is eliminated, and the contribution rate of variance is taken as the weight of the corresponding principal component. On this basis, SWDTW, is used to measure the similarity between multiple time series. Finally, similarity search experiments show that CPCA-SWDTW has good accuracy and robustness. Sensitivity analysis shows that CPCA-SWDTW is influenced by the parameters of weight function to some extent.
【作者單位】： 國防科學(xué)技術(shù)大學(xué)信息系統(tǒng)與管理學(xué)院;
【基金】：國家自然科學(xué)基金項目(No.71671186,71571185,71501182)資助~~
【分類號】：TP311.13
[Abstract]:Aiming at the problem of ignoring variable correlation and local shape characteristics in common methods, a multi-variable time series similarity matching method (CPCA-SWDTW) based on weighted dynamic time bending is proposed. First of all, based on the original weighted dynamic time bending algorithm and the introduction of morphological factor, a weighted dynamic time bending algorithm (SWDTW). Based on morphological features is proposed. Then, the principal component of the multivariate time series is extracted as the pattern representation, and the correlation between variables is eliminated, and the contribution rate of variance is taken as the weight of the corresponding principal component. On this basis, SWDTW, is used to measure the similarity between multiple time series. Finally, similarity search experiments show that CPCA-SWDTW has good accuracy and robustness. Sensitivity analysis shows that CPCA-SWDTW is influenced by the parameters of weight function to some extent.
【作者單位】： 國防科學(xué)技術(shù)大學(xué)信息系統(tǒng)與管理學(xué)院;
【基金】：國家自然科學(xué)基金項目(No.71671186,71571185,71501182)資助~~
【分類號】：TP311.13
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