Abstract
The emergence of time-series foundation model research elevates the growing need to measure the (dis)similarity of time-series datasets. A time-series dataset similarity measure aids research in multiple ways, including model selection, finetuning, and visualization. In this paper, we propose a distribution-based method to measure time-series dataset similarity by leveraging the Wasserstein distance. We consider a time-series dataset an empirical instantiation of an underlying multivariate normal distribution (MVN). The similarity between two time-series datasets is thus computed as the Wasserstein distance between their corresponding MVNs. Comprehensive experiments and visualization show the effectiveness of our approach. Specifically, we show how the Wasserstein distance helps identify similar time-series datasets and facilitates inference performance estimation of foundation models in both out-of-distribution and transfer learning evaluation, with high correlations between our proposed measure and the inference loss (>0.60).
Abstract (translated)
时间序列基础模型研究的出现提高了衡量时间序列数据集相似度的需求。一种时间序列数据集的相似性测量方法有助于多种研究方式，包括模型选择、微调和可视化等。在本文中，我们提出了一种基于分布的方法来利用Wasserstein距离（也称为地球移动距离）来衡量时间序列数据集之间的相似性。我们将一个时间序列数据集视为潜在多变量正态分布（MVN）的实证实例，并将两个时间序列数据集间的相似度计算为它们对应的MVNs之间的Wasser斯坦距离。 全面的实验和可视化展示了我们方法的有效性。具体而言，我们演示了Wasserstein距离如何帮助识别相似的时间序列数据集，并在分布外(OOD)评估及迁移学习中辅助基础模型推理性能估计，我们的衡量标准与推断损失之间具有高度的相关性（>0.60）。
URL
https://arxiv.org/abs/2507.22189
