A new shape-based clustering algorithm for time series

Yucheng Li, Derong Shen, Tiezheng Nie, Yue Kou

Abstract: Highlights•We propose to use fractional order correlation to measure the relationship between two sequences and apply the normalized form of the results to create the fractional order shape-based distance between two sequences.•We use the average sequence calculation method DBA to determine the cluster center and then combine it with our distance to cluster the time series.•We also combine our distance with the center determination strategy of other clustering algorithms to execute comparative experiments.•Experiments show that our method has improved the clustering accuracy. Our proposed distance can achieve better results when combined with a variety of strategies. At the same time, in the shape-based clustering algorithm, compared with the best KShape algorithm, we can also achieve better results.