  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.
