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arXiv · 2503.11411

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

Xu Liu·Taha Aksu·Juncheng Liu·Qingsong Wen·Yuxuan Liang·Caiming Xiong·Silvio Savarese·Doyen Sahoo·Junnan Li·Chenghao Liu
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Abstract

Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), enabling generalized learning and integrating contextual information. However, their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. Synthetic data emerge as a viable solution, addressing these challenges by offering scalable, unbiased, and high-quality alternatives. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.

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Xu Liu, Taha Aksu, Juncheng Liu, Qingsong Wen, Yuxuan Liang, Caiming Xiong, Silvio Savarese, Doyen Sahoo, Junnan Li, Chenghao Liu. 2025-03-14. Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models. https://arxiv.org/abs/2503.11411

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