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Classification of Uncertain Time Series by Propagating Uncertainty in Shapelet Transform

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, the uncertainty in data is not explicitly taken into account by these methods. Using uncertainty propagation techniques, we propose a new uncertain dissimilarity measure based on euclidean distance. We also show how to classify uncertain time series using the proposed dissimilarity measure and shapelet transform, one of the best time series classification methods. An experimental assessment of our contribution is done on the well known UCR dataset.
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https://hal.uca.fr/hal-03099346
Contributor : Michael Mbouopda <>
Submitted on : Wednesday, January 6, 2021 - 10:15:41 AM
Last modification on : Thursday, January 7, 2021 - 3:28:49 AM

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Michael Mbouopda, Engelbert Mephu Nguifo. Classification of Uncertain Time Series by Propagating Uncertainty in Shapelet Transform. ECML/PKDD 2020 Tutorial and Workshop on Uncertainty in Machine Learning, 2020. ⟨hal-03099346⟩

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