关键词: 时间序列聚类, 特征时间段抽取, 互相关函数

Abstract: Based on cross-correlation, an efficient, fast method is proposed for time series clustering and the time series clustering is realized by a two steps measure. The first step is based on symbolic of time series and extracts the characteristic time period by designing a characteristic extraction algorithm. The second step is based on cross-correlation, which realizes a faster time series clustering by adjusting the cross-correlation step. The experiments show that this method can fit sparse and dense time series data extraction. Comparing with traditional clustering distance measure, this method has high processing speed and can perform better on the stretch of time series shape. Meanwhile, this method keeps the accuracy in a high degree.

Key words: time series clustering, characteristic period extraction, cross-correlation