A Survey on Data Generation for Time Series: Taxonomy, Review and Prospects

Time series generation (TSG) plays a fundamental role in data engineering and knowledge discovery, serving as a key enabler for data augmentation, representation learning, privacy preservation, and scenario simulation analysis in temporal data mining. By synthesizing realistic and controllable time series, TSG directly benefits downstream tasks such as forecasting, anomaly detection, and classification by data augmentation and improving model generalization. This survey presents a comprehensive and systematic review of TSG methodologies, spanning traditional non-deep learning approaches, such as rule-based, statistical, and simulation-based methods, and modern deep generative models, including variational autoencoders, generative adversarial networks, diffusion models, normalizing flows, and large language models. To organize the rapidly growing literature, we introduce a unified multi-level taxonomy to organize the technical landscape of TSG and clarify the relationships among diverse approaches. The taxonomy emphasizes underlying modeling principles rather than individual representative models. Specifically, it categorizes TSG methods according to modeling backbones, generation settings (conditional versus unconditional), sampling regularity, and modality characteristics. Building on this taxonomy, we systematically review advanced methods within each category and discuss how they address key challenges such as irregular sampling, long-sequence modeling, multivariate dependencies, and controllable generation, and how they are applied in real-world scenarios. Furthermore, we present a holistic evaluation framework encompassing five core dimensions, including fidelity, diversity, controllability, downstream performance, and privacy protection. Finally, we identify critical open challenges and outline promising research directions from data characteristics, application scenarios, and modeling paradigm perspectives. This survey aims to serve as a structured reference and roadmap for researchers and practitioners in this rapidly evolving field.