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

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

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 · ORCID
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 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.AI 2026-07 conditional novelty 6.0 of 10

    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. Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

    cs.LG 2026-06 conditional novelty 6.0 of 10

    A spatio-temporal foundation model pre-trained exclusively on synthetic stochastic graph dynamics outperforms real-data-pretrained STFMs in zero-shot traffic forecasting, according to the paper's benchmarks.

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

    eess.SP 2026-04 unverdicted novelty 6.0 of 10

    TimeRFT fine-tunes time-series foundation models with step-wise reward signals and difficulty-filtered data, beating supervised fine-tuning on eight benchmarks across data regimes.

  4. Parallel Complex Diffusion for Scalable Time Series Generation

    cs.LG 2026-02 conditional novelty 5.0 of 10

    PaCoDi generates time series by diffusing real and imaginary spectral components in parallel, cutting attention FLOPs roughly in half while improving benchmark scores.

  5. A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models

    cs.AI 2025-09 conditional novelty 5.0 of 10

    The authors organize LLM-based time series reasoning into three exclusive topologies (direct, chain, branch) crossed with four objectives, and use them to label 125 papers, benchmarks, and resources.

  6. Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era

    cs.LG 2025-08 unverdicted novelty 1.0 of 10

    A tutorial proposal outlining how generative models can synthesize data across modalities for data mining, with no new research results.

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