# TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

arXiv: https://arxiv.org/abs/2501.05403 · HTML: https://mlsanity.com/paper/2501.05403/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://mlsanity.com/paper/2403.01742/) (2024)
- [TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation](https://mlsanity.com/paper/2111.08095/) (2021)
- [Empowering Time Series Analysis with Large Language Models: A Survey](https://mlsanity.com/paper/2402.03182/) (2024)
- [Vector Quantized Time Series Generation with a Bidirectional Prior Model](https://mlsanity.com/paper/2303.04743/) (2023)
- [Causal Recurrent Variational Autoencoder for Medical Time Series Generation](https://mlsanity.com/paper/2301.06574/) (2023)
- [BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis](https://mlsanity.com/paper/2310.20496/) (2023)
- [MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process](https://mlsanity.com/paper/2403.05751/) (2024)
- [Towards Realistic Market Simulations: a Generative Adversarial Networks Approach](https://mlsanity.com/paper/2110.13287/) (2021)
- [Controllable Financial Market Generation with Diffusion Guided Meta Agent](https://mlsanity.com/paper/2408.12991/) (2024)

## Built on by (most cited first, 21 of 21)

- [BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling](https://mlsanity.com/paper/2503.02445/) (2025)
- [From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization](https://mlsanity.com/paper/2508.09191/) (2025)
- [TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation](https://mlsanity.com/paper/2504.17613/) (2025)
- [Causal Time Series Generation via Diffusion Models](https://mlsanity.com/paper/2509.20846/) (2025)
- [Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution](https://mlsanity.com/paper/2510.19166/) (2025)
- [HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2512.10807/) (2025)
- [OATS: Online Data Augmentation for Time Series Foundation Models](https://mlsanity.com/paper/2601.19040/) (2026)
- [Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations](https://mlsanity.com/paper/2601.13534/) (2026)
- [Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment](https://mlsanity.com/paper/2506.01290/) (2025)
- [Instruction-based Time Series Editing](https://mlsanity.com/paper/2508.01504/) (2025)
- [Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review](https://mlsanity.com/paper/2504.18544/) (2025)
- [AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt](https://mlsanity.com/paper/2512.24625/) (2025)
- [TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization](https://mlsanity.com/paper/2605.01418/) (2026)
- [Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series](https://mlsanity.com/paper/2605.22055/) (2026)
- [TriHead-GAN: A Generative Adversarial Network with Triple-Head Discriminator for Carbon Emission Time Series Generation](https://mlsanity.com/paper/2606.07569/) (2026)
- [UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation](https://mlsanity.com/paper/2606.10466/) (2026)
- [Towards a Unified Generative Model for Scarce Time Series with Domain Experts](https://mlsanity.com/paper/2606.15172/) (2026)
- [MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters](https://mlsanity.com/paper/2608.23473/) (2026)
- [ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation](https://mlsanity.com/paper/2609.24003/) (2026)
- [HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling](https://mlsanity.com/paper/2609.34511/) (2026)
- [ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation](https://mlsanity.com/paper/2609.37632/) (2026)

## Abstract (arXiv)

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to work in other application areas, this approach remains challenging for time series modeling due to the large divergence in patterns among different real world time series categories. In this paper, we propose a multi-domain time series diffusion model with domain prompts, named TimeDP. In TimeDP, we utilize a time series semantic prototype module which defines time series prototypes to represent time series basis, each prototype vector serving as "word" representing some elementary time series feature. A prototype assignment module is applied to extract the extract domain specific prototype weights, for learning domain prompts as generation condition. During sampling, we extract "domain prompt" with few-shot samples from the target domain and use the domain prompts as condition to generate time series samples. Experiments demonstrate that our method outperforms baselines to provide the state-of-the-art in-domain generation quality and strong unseen domain generation capability.