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arxiv 2503.11411 v1 pith:YLSG6474 submitted 2025-03-14 cs.LG

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
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Junnan Li Chenghao Liu
This is my paper
classification cs.LG
keywords datamodelsseriestimefoundationsyntheticanalysishigh-quality
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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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Cited by 3 Pith papers

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  1. RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models

    cs.AI 2026-07 conditional novelty 6.0

    A curated 142-billion-point real-world multivariate time series corpus improves zero-shot forecasting when combined with existing synthetic and univariate pretraining data across four foundation models.

  2. TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

    cs.AI 2026-06 unverdicted novelty 6.0

    TRACE proposes a temporal conditional estimation paradigm for multimodal time series foundation models that infers incomplete target modalities from auxiliary ones, outperforming prior fusion methods on clinical and s...

  3. TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning

    eess.SP 2026-04 unverdicted novelty 5.0

    TimeRFT applies reinforcement learning with multi-faceted step-wise rewards and informative sample selection to improve generalization and accuracy in TSFM adaptation beyond supervised fine-tuning.