Uncertain Time Series Classification With Shapelet Transform

• URL: http://arxiv.org/abs/2102.02090v1
• Date: Wed, 3 Feb 2021 14:46:01 GMT
• Title: Uncertain Time Series Classification With Shapelet Transform
• Authors: Michael Franklin Mbouopda and Engelbert Mephu Nguifo
• Abstract summary: Time series classification is a task that aims at classifying chronological data. We propose a new uncertain dissimilarity measure based on Euclidean distance. We then propose the uncertain shapelet transform algorithm for the classification of uncertain time series.
• Score: 1.4467794332678539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series classification is a task that aims at classifying chronological data. It is used in a diverse range of domains such as meteorology, medicine and physics. In the last decade, many algorithms have been built to perform this task with very appreciable accuracy. However, applications where time series have uncertainty has been under-explored. Using uncertainty propagation techniques, we propose a new uncertain dissimilarity measure based on Euclidean distance. We then propose the uncertain shapelet transform algorithm for the classification of uncertain time series. The large experiments we conducted on state of the art datasets show the effectiveness of our contribution. The source code of our contribution and the datasets we used are all available on a public repository.

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