# Uncertain Time Series Classification With Shapelet Transform

arXiv: https://arxiv.org/abs/2102.02090 · HTML: https://mlsanity.com/paper/2102.02090/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)

## Built on by (most cited first, 1 of 1)

- [Explainable classification of astronomical uncertain time series](https://mlsanity.com/paper/2210.00869/) (2022)

## Abstract (arXiv)

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.