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In this paper, we describe a universal method for extracting the underlyingmonotonic trend factor from time series data. We propose an approach related tothe Mann-Kendall test, a standard monotonic trend detection method and call itcontrastive trend estimation (CTE). We show that the CTE method identifies anyhidden trend underlying temporal data while avoiding the standard assumptionsused for monotonic trend identification. In particular, CTE can take any typeof temporal data (vector, images, graphs, time series, etc.) as input. Wefinally illustrate the interest of our CTE method through several experimentson different types of data and problems.

Universal hidden monotonic trend estimation with contrastive learning