Frobenius correlation based u-shapelets discovery for time series clustering

作者：

Highlights：

• We review state of the art on similarity functions for uncertain time Series and evaluate them for the comparison of small, uncertain time series.

• We introduce the Frobenius cOrrelation for uncertain Time series ushapelet discovery (FOTS), a new dissimilarity score based on local correlation, which has interesting properties useful for comparison of small, uncertain time series and that makes no assumption on the probability distribution of uncertainty in data.

• We evaluate FOTS on 63 datasets on clustering task.

• We put the source code at the disposal of the scientific community to allow extension of our work.

摘要

•We review state of the art on similarity functions for uncertain time Series and evaluate them for the comparison of small, uncertain time series.•We introduce the Frobenius cOrrelation for uncertain Time series ushapelet discovery (FOTS), a new dissimilarity score based on local correlation, which has interesting properties useful for comparison of small, uncertain time series and that makes no assumption on the probability distribution of uncertainty in data.•We evaluate FOTS on 63 datasets on clustering task.•We put the source code at the disposal of the scientific community to allow extension of our work.

论文关键词：Clustering,UShapelet,Correlation,Time series

论文评审过程：Received 3 October 2018, Revised 14 December 2019, Accepted 23 February 2020, Available online 28 February 2020, Version of Record 6 March 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107301