  In this paper, we describe a universal method for extracting the underlying
monotonic trend factor from time series data. We propose an approach related to
the Mann-Kendall test, a standard monotonic trend detection method and call it
contrastive trend estimation (CTE). We show that the CTE method identifies any
hidden trend underlying temporal data while avoiding the standard assumptions
used for monotonic trend identification. In particular, CTE can take any type
of temporal data (vector, images, graphs, time series, etc.) as input. We
finally illustrate the interest of our CTE method through several experiments
on different types of data and problems.
