Uncertain Time Series Classification With Shapelet Transform

TL;DR

This paper introduces an uncertain shapelet transform algorithm for classifying uncertain time series, leveraging uncertainty propagation to improve accuracy in domains like meteorology and medicine.

Contribution

It proposes a novel uncertain dissimilarity measure and shapelet transform method specifically designed for uncertain time series classification.

Findings

Effective on state-of-the-art datasets

Demonstrates improved accuracy over existing methods

Provides open-source code and datasets

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.