Classification des S{é}ries Temporelles Incertaines par Transformation Shapelet
This work addresses time series classification in domains like meteorology and medicine where data uncertainty is common, offering an incremental improvement by incorporating uncertainty into existing shapelet methods.
The paper tackles the problem of classifying uncertain time series by proposing a new dissimilarity measure based on Euclidean distance with uncertainty propagation, adapting shapelet transformation for this purpose, and demonstrates its effectiveness through experimental assessment on state-of-the-art datasets.
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.