论文标题
与对比度学习的通用隐藏单调趋势估计
Universal hidden monotonic trend estimation with contrastive learning
论文作者
论文摘要
在本文中，我们描述了一种通用方法，用于从时间序列数据中提取潜在的单调趋势因子。我们提出了一种与Mann-Kendall测试有关的方法，Mann-Kendall测试是一种标准单调趋势检测方法，并将其称为对比度趋势估计（CTE）。我们表明，CTE方法可以识别时间数据的任何隐藏趋势，同时避免使用单调趋势识别的标准假设。特别是，CTE可以将任何类型的时间数据（向量，图像，图形，时间序列等）作为输入。我们最终通过对不同类型的数据和问题进行了几项实验来说明我们的CTE方法的兴趣。
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.
