# TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting

arXiv: https://arxiv.org/abs/2412.20810 · HTML: https://mlsanity.com/paper/2412.20810/

## Key facts

- Cited by 17 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 13 of its 38 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Placed in

- Retrieval-augmented generation / [Time series and agricultural RAG](https://mlsanity.com/rag/time-series-and-agricultural-rag/)

## Builds on (most cited first, 13 of 13)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [DocPrompting: Generating Code by Retrieving the Docs](https://mlsanity.com/paper/2207.05987/) (2022)
- [Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2310.08278/) (2023)
- [Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting](https://mlsanity.com/paper/2306.11025/) (2023)
- [Retrieval Based Time Series Forecasting](https://mlsanity.com/paper/2209.13525/) (2022)
- [RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting](https://mlsanity.com/paper/2403.04180/) (2024)

## Built on by (most cited first, 17 of 17)

- [TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval](https://mlsanity.com/paper/2506.09114/) (2025)
- [Large Language models for Time Series Analysis: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2506.11040/) (2025)
- [Post-Training in Time Series Foundation Models: A Unifying Framework](https://mlsanity.com/paper/2607.20002/) (2026)
- [EventTSF: Event-Aware Non-Stationary Time Series Forecasting](https://mlsanity.com/paper/2508.13434/) (2025)
- [Semantics-Enhanced Retrieval-Augmented Time Series Forecasting](https://mlsanity.com/paper/2606.14941/) (2026)
- [WPBench: A Comprehensive Benchmark for Wind Power Forecasting](https://mlsanity.com/paper/2609.24444/) (2026)
- [Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2508.17009/) (2025)
- [Retrieval-Augmented Generation with Covariate Time Series](https://mlsanity.com/paper/2603.04951/) (2026)
- [Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting](https://mlsanity.com/paper/2605.24911/) (2026)
- [Dynamic Perturbed Adaptive Method for Infinite Task-Conflicting Time Series](https://mlsanity.com/paper/2505.11902/) (2025)
- [DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting](https://mlsanity.com/paper/2601.07250/) (2026)
- [Align-RAG: Alignment Is All You Need for TSFM In-Context Learning](https://mlsanity.com/paper/2608.05571/) (2026)
- [Model-agnostic Retrieval-Augmented Extended Forecasting for time series](https://mlsanity.com/paper/2608.14054/) (2026)
- [Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting](https://mlsanity.com/paper/2609.21381/) (2026)
- [GNA: Granular Neighbor Assembly for Retrieval-Augmented Multivariate Time-Series Forecasting](https://mlsanity.com/paper/2609.36281/) (2026)
- [GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory](https://mlsanity.com/paper/2610.04827/) (2026)
- [ECHO: Correcting Time Series Foundation Model Forecasts with Historical Analogues and Errors](https://mlsanity.com/paper/2610.16433/) (2026)

## Abstract (arXiv)

Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Retrieval-Augmented Generation (RAG) methods have been widely employed to enhance the performance of foundation models on unseen data, allowing models to access to external knowledge. In this paper, we introduce TimeRAF, a Retrieval-Augmented Forecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques. We develop customized time series knowledge bases that are tailored to the specific forecasting tasks. TimeRAF employs an end-to-end learnable retriever to extract valuable information from the knowledge base. Additionally, we propose Channel Prompting for knowledge integration, which effectively extracts relevant information from the retrieved knowledge along the channel dimension. Extensive experiments demonstrate the effectiveness of our model, showing significant improvement across various domains and datasets.