Classification des S{\'e}ries Temporelles Incertaines par Transformation Shapelet

TL;DR

This paper introduces a novel shapelet transformation method for classifying uncertain time series data by incorporating data uncertainty through a new dissimilarity measure based on Euclidean distance.

Contribution

It proposes a new dissimilarity measure that accounts for data uncertainty and adapts shapelet transformation for uncertain time series classification.

Findings

Improved classification accuracy on state-of-the-art datasets

Effective handling of data uncertainty in time series classification

Demonstrated the method's robustness across different datasets

Abstract

Time serie classification is used in a diverse range of domain such as meteorology, medicine and physics. It aims to classify chronological data. Many accurate approaches have been built during the last decade and shapelet transformation is one of them. However, none of these approaches does take data uncertainty into account. Using uncertainty propagation techiniques, we propose a new dissimilarity measure based on euclidean distance. We also show how to use this new measure to adapt shapelet transformation to uncertain time series classification. An experimental assessment of our contribution is done on some state of the art datasets.