Uncertain Time Series Classification With Shapelet Transform
Autor: | Mbouopda, Michael Franklin, Nguifo, Engelbert Mephu |
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Rok vydání: | 2021 |
Předmět: | |
Zdroj: | 2020 IEEE International Conference on Data Mining Workshops (ICDMW) |
Druh dokumentu: | Working Paper |
DOI: | 10.1109/ICDMW51313.2020.00044 |
Popis: | 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. Comment: 2020 International Conference on Data Mining Workshops, Sorrento, Italy, November 17-20, 2020 |
Databáze: | arXiv |
Externí odkaz: |
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