  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.

Machine Learning;Artificial Intelligence