DiffCATS: Causally Associated Time-Series Generation through Diffusion Models

TMLR Paper6294 Authors

Abstract: Understanding the intrinsic causal structure of time-series data is crucial for effective real-world interventions and decision-making, but progress in Time-Series Causal Discovery (TSCD) is often limited by the lack of high-quality datasets with diverse and realistic temporal causal relationships. This highlights the need to provide synthetic time-series generation tools, with realism as a primary objective, an aspect that requires incorporating causal relationships beyond mere correlation. To address this challenge, we propose a diffusion model called DiffCATS. It simultaneously generates multiple causally associated time-series as well as a ground truth causal graph that reflects their mutual temporal dependencies, requiring only observational time-series data for training. Experiments demonstrate that it outperforms state-of-the-art methods in producing realistic time-series with causal graphs that closely resemble those of real-world phenomena. We highlight the practical utility of our data on three downstream tasks, including benchmarking widely used TSCD algorithms.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Fabio_Stella1

Submission Number: 6294