# Retrieval-Augmented Diffusion Models for Time Series Forecasting

arXiv: https://arxiv.org/abs/2410.18712 · HTML: https://mlsanity.com/paper/2410.18712/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 33 of its 47 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Placed in

- Retrieval-augmented generation / [Time series and agricultural RAG](https://mlsanity.com/rag/time-series-and-agricultural-rag/)
- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation](https://mlsanity.com/paper/2107.03502/) (2021)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model](https://mlsanity.com/paper/2304.01116/) (2023)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Frequency-domain MLPs are More Effective Learners in Time Series Forecasting](https://mlsanity.com/paper/2311.06184/) (2023)
- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://mlsanity.com/paper/2301.03028/) (2023)
- [Non-autoregressive Conditional Diffusion Models for Time Series Prediction](https://mlsanity.com/paper/2306.05043/) (2023)
- [MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process](https://mlsanity.com/paper/2403.05751/) (2024)
- [DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting](https://mlsanity.com/paper/2203.07681/) (2022)

## Built on by (most cited first, 30 of 39)

- [TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster](https://mlsanity.com/paper/2503.07649/) (2025)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Diffusion Models for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2507.14507/) (2025)
- [Multimodal Conditioned Diffusive Time Series Forecasting](https://mlsanity.com/paper/2504.19669/) (2025)
- [TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval](https://mlsanity.com/paper/2506.09114/) (2025)
- [Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval](https://mlsanity.com/paper/2602.10847/) (2026)
- [TS-Memory: Plug-and-Play Memory for Time Series Foundation Models](https://mlsanity.com/paper/2602.11550/) (2026)
- [RealDrive: Retrieval-Augmented Driving with Diffusion Models](https://mlsanity.com/paper/2505.24808/) (2025)
- [LayoutRAG: Retrieval-Augmented Model for Content-agnostic Conditional Layout Generation](https://mlsanity.com/paper/2506.02697/) (2025)
- [Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades](https://mlsanity.com/paper/2508.04888/) (2025)
- [Channel-wise Retrieval for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2604.05543/) (2026)
- [FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance](https://mlsanity.com/paper/2606.03184/) (2026)
- [How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control](https://mlsanity.com/paper/2506.05276/) (2025)
- [Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction](https://mlsanity.com/paper/2510.24049/) (2025)
- [RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction](https://mlsanity.com/paper/2605.08857/) (2026)
- [Spectral Retrieval-Augmented Time-Series Forecasting](https://mlsanity.com/paper/2606.19412/) (2026)
- [ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation](https://mlsanity.com/paper/2607.07640/) (2026)
- [ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series](https://mlsanity.com/paper/2607.12391/) (2026)
- [Multimodal Forecasting of Sparse Intraoperative Hypotension Events Powered by Language Model](https://mlsanity.com/paper/2505.22116/) (2025)
- [CHIME: Conditional Hallucination and Integrated Multi-scale Enhancement for Time Series Diffusion Model](https://mlsanity.com/paper/2506.03502/) (2025)
- [EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting](https://mlsanity.com/paper/2512.15067/) (2025)
- [Enhancing few-shot time series forecasting with LLM-guided diffusion](https://mlsanity.com/paper/2602.00040/) (2026)
- [GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search](https://mlsanity.com/paper/2602.15423/) (2026)
- [Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs](https://mlsanity.com/paper/2603.19970/) (2026)
- [Modular Retrieval-Augmented Generalization for Human Action Recognition](https://mlsanity.com/paper/2605.08117/) (2026)
- [Diachronic Sample Integration: Robust Tail-Risk Estimation with Generative Models](https://mlsanity.com/paper/2607.10810/) (2026)
- [The Spectrum Is Not Enough: When Context Helps Time-Series Forecasting](https://mlsanity.com/paper/2607.13006/) (2026)
- [PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling](https://mlsanity.com/paper/2607.20230/) (2026)
- [StrideDiffusion: Accelerating Diffusion Models for Time-series Generation](https://mlsanity.com/paper/2607.20545/) (2026)
- [Align-RAG: Alignment Is All You Need for TSFM In-Context Learning](https://mlsanity.com/paper/2608.05571/) (2026)

## Abstract (arXiv)

While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series diffusion models include insufficient time series datasets and the absence of guidance. To address these limitations, we propose a Retrieval- Augmented Time series Diffusion model (RATD). The framework of RATD consists of two parts: an embedding-based retrieval process and a reference-guided diffusion model. In the first part, RATD retrieves the time series that are most relevant to historical time series from the database as references. The references are utilized to guide the denoising process in the second part. Our approach allows leveraging meaningful samples within the database to aid in sampling, thus maximizing the utilization of datasets. Meanwhile, this reference-guided mechanism also compensates for the deficiencies of existing time series diffusion models in terms of guidance. Experiments and visualizations on multiple datasets demonstrate the effectiveness of our approach, particularly in complicated prediction tasks.