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

2025-03-14

AI Summary

Time-series foundation models (TSFMs) and large language model–driven time-series models (TSLLMs) suffer from scarcity of high-quality, diverse real-world time-series data. Method: We systematically investigate the role of synthetic data in pretraining, fine-tuning, and evaluation of TSFMs/TSLLMs. We integrate generative approaches—including GANs, VAEs, diffusion models, LLM-based generation, and prompt-driven synthesis—while explicitly modeling temporal characteristics such as periodicity, abrupt changes, and multi-scale dependencies. Contribution/Results: We propose the first methodology framework for synthetic data in time-series AI, mapping generation strategies to model capability improvements. We categorize seven mainstream synthetic methods and four core application scenarios, identify six critical research gaps, and advocate a future paradigm emphasizing scalability, debiasing, and fidelity. This work delivers the first comprehensive roadmap for synthetic-data–driven time-series AI.

Technology Category

Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Time-Series/Data StreamsPlanning, Routing, and Scheduling: Temporal Planning

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsWeb Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Addressing dataset challenges for time series analysis models
• Exploring synthetic data for scalable and unbiased alternatives
• Reviewing synthetic data's role in model training and evaluation

Innovation

• Synthetic data enhances time series analysis.
• Foundation models enable generalized learning.
• Data generation strategies improve model training.