Title: TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time Series Forecasting; Foundation Model; Retrieval-Augmented Generation
Abstract: Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they often struggle to generalize across diverse and unseen datasets. Moreover, existing Time Series Foundation Models (TSFMs) still face challenges in handling non-stationary dynamics and distribution shifts, largely due to the lack of effective mechanisms for adaptation. To this end, we present TS-RAG, a retrieval-augmented generation framework for time series forecasting that enhances the generalization and interpretability of TSFMs. Specifically, TS-RAG leverages pre-trained time series encoders to retrieve semantically relevant segments from a dedicated knowledge base, enriching the contextual representation of the input query. Furthermore, we propose an Adaptive Retrieval Mixer (ARM) module that dynamically fuses the retrieved patterns with the TSFM's internal representation, improving forecasting accuracy without requiring task-specific fine-tuning. Thorough empirical studies on seven public benchmark datasets demonstrate that TS-RAG achieves state-of-the-art zero-shot forecasting performance, outperforming the existing TSFMs by up to 6.84\% across diverse domains while also providing desirable interpretability. Our code and data are available at: https://github.com/UConn-DSIS/TS-RAG.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces TS-RAG, a retrieval-augmented generation framework for time series forecasting, designed to improve the zero-shot performance and interpretability of Time Series Foundation Models (TSFMs).\nTSFMs and LLMs have shown promise in forecasting tasks. However, they often struggle with generalization across diverse domains and suffer from limited adaptability to non-stationary or distribution-shifting time series, especially in zero-shot scenarios. Moreover, interpretability remains limited.\nTS-RAG enhances TSFMs by introducing a RAG mechanism tailored for time series forecasting. It retrieves semantically similar time series segments from a pre-built knowledge base using a pretrained retrieval encoder. These retrieved segments are then fused with the current input via an Adaptive Retrieval Mixer (ARM), which dynamically weighs and integrates retrieved knowledge to improve prediction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The design of TS-RAG is well-motivated and implemented. The combination of a retrieval encoder, a frozen TSFM backbone, and a lightweight ARM (Adaptive Retrieval Mixer) module is effective and modular.\n2. The authors evaluate TS-RAG on seven diverse benchmark datasets and conduct multiple ablation studies, cross-domain evaluations, and comparisons with strong baselines, including Chronos-Bolt, MOMENT, and RAF. Besides, TS-RAG shows consistent performance improvements, with up to 6.84% MSE reduction over TSFMs in zero-shot settings.\n\n\nWeaknesses:\n\n1. While results on Chronos-Bolt and MOMENT are included, more diversity in TSFM architectures (e.g., TimeGPT, Time-MoE) would strengthen generalizability claims.\n2. The assumption that 5M–26M samples sampled from Chronos’ dataset are representative may not hold in all domains (e.g., healthcare, rare events).\n3. While the ARM is technically promising, the ablation gains over simpler fusion strategies (e.g., gating) are moderate. The overhead of ARM design might not be justified in all settings.\n\nQUESTIONS:\n1. While TS-RAG is described as a plug-and-play module, only two backbones (Chronos-Bolt and MOMENT) are evaluated.\n\n2. The ARM module introduces a moderately complex multi-stage mixing mechanism. Gains over simpler fusion methods (e.g., gating) in Table 7 are present but not substantial across all datasets.\n\nLIMITATIONS:\n1. The retrieval knowledge base is constructed from pretraining datasets (e.g., Chronos) without clear discussion on data representativeness, domain coverage, or temporal bias.\n\n2. The method assumes the availability of large-scale time series data for retrieval, which may not be feasible in privacy-sensitive applications (e.g., patient monitoring).\n\n3. There is no mechanism to verify the validity of retrieved sequences or assess if retrievals come from trustworthy sources.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents TS-RAG, a retrieval-augmented generation framework for time series forecasting that enhances the generalization and interpretability of Time Series Foundation Models (TSFMs). It leverages pre-trained time series encoders to retrieve semantically relevant segments from a dedicated knowledge base and employs an Adaptive Retrieval Mixer (ARM) module to dynamically fuse the retrieved patterns with the TSFM’s internal representation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Enhances model interpretability through retrieval-as-evidence and transparent weighting of retrieved patterns\n- Avoids task-specific fine-tuning, reducing computational overhead while maintaining strong generalization\n- Compatible with various TSFMs (e.g., Chronos-bolt, MOMENT), demonstrating flexible plug-and-play design\n\nWeakness:\n- Performance depends heavily on the retrieval knowledge base composition\n- Long-horizon forecasting relies on rolling strategies, risking error accumulation\n\nQUESTIONS:\n1. In the abstract, you claim that TS-RAG \"provides desirable interpretability,\" but the case studies mainly focus on retrieval similarity. Could you elaborate on how the model's internal decision-making process (e.g., attention weights in ARM) enhances interpretability beyond retrieving similar segments?\n\n2. You mention that TS-RAG outperforms existing TSFMs by up to 6.84% across diverse domains. However, the improvement varies across datasets (e.g., smaller gains in ETTh2 vs. larger gains in ETTm1). What specific characteristics of time series data (e.g., stationarity, noise level) make TS-RAG more effective, and could you provide a quantitative analysis of this correlation?\n\n3. The paper states that TS-RAG is compatible with any general TSFM, but experiments primarily use Chronos-bolt and MOMENT. Have you tested it with other architectures like Lag-Llama or TimeGPT-1, and if so, what performance trends were observed? If not, what challenges might arise when adapting TS-RAG to these models?\n\n4. You note that in-domain retrieval achieves the lowest error, but real-world scenarios often lack in-domain data. Could you discuss strategies to optimize TS-RAG's performance when only cross-domain or limited knowledge bases are available?\n\n5. The ablation study on the number of retrieved sequences (k) shows diminishing returns beyond a threshold. However, the optimal k varies by dataset. Is there a dynamic k-selection mechanism that TS-RAG could adopt to adapt to different time series patterns, and what would its design entail?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces TS-RAG, a retrieval-augmented framework that plugs time-series foundation models into a fast nearest-neighbour search to improve zero-shot forecasting. It first embeds the query’s recent history with a frozen encoder, retrieves the k most similar past windows, and feeds the corresponding true future segments into an Adaptive Retrieval Mixer (ARM). ARM learns attention weights that blend these historical outcomes with the backbone model’s own prediction, while keeping both the backbone and the retriever fixed. Evaluated on seven standard benchmarks, TS-RAG cuts mean-squared and absolute errors and delivers lower inference latency than a prior retrieval baseline.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\nS1. TS-RAG shows how to graft the RAG idea onto generic TSFMs, letting a forecaster pull in semantically similar historical windows and use them during inference.\n\nS2. The proposed ARM block learns attention weights between the query embedding and each retrieved future, then gates them against the backbone forecast, yielding consistent gains over simpler fusions.\n\nS3. Plug-and-play design can generalise across backbones and retriever encoders. Only the projector and ARM are trained, and both the backbone TSFM and the retriever encoder remain frozen.\n\n**Weaknesses:**\n\nW1. The retriever ranks neighbours only by Euclidean distance, which only focuses on the point-level similarity. No exploration of DTW, cosine, or learned metrics, so robustness to misalignment/regime shift is unknown.\n\nW2. Only deterministic point-forecast errors (MSE/MAE) are reported; probabilistic scores are absent, so uncertainty calibration is untested.\n\nW3. Best results come when the knowledge base is built from the training split of the same dataset. Cross-domain retrieval is weaker, showing the method needs closely matched data at inference time.\n\nQUESTIONS:\nQ1. Nearest neighbours are ranked only by Euclidean distance in embedding space. This may fail under temporal misalignment or regime shifts. Report results for at least one stronger retrieval scheme: cosine similarity, DTW-aware embeddings, or a learned metric layer.\n\nQ2. TS-RAG combines diverse historical futures but only reports MSE/MAE. We do not know whether the method yields well-calibrated predictive distributions. Add at least one proper probabilistic metric comparing TS-RAG to the backbone and RAF.\n\nQ3. The retrieval index is built from the training split of the same benchmark, so windows that are temporally close but still overlap in pattern with the test set might be retrieved. Please report the degree of overlap between the index and each test split, and re-run TS-RAG with an index built strictly from other datasets or from earlier, non-overlapping time blocks if necessary.\n\nLIMITATIONS:\n1. Explain whether the retrieval component could amplify historical biases (e.g., over-representing dominant markets) and propose mitigation (balanced retrieval sampling, debiasing losses)", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TS-RAG, a novel retrieval-augmented generation framework for time series forecasting, with a focus on addressing the generalization and interpretability challenges of Time Series Foundation Models. The key innovation lies in combining a pretrained retriever encoder with an Adaptive Retrieval Mixer module to dynamically integrate retrieved relevant time series segments to improve zero-shot forecasting performance without fine-tuning. The empirical study over seven benchmark datasets demonstrates the superiority of TS-RAG over state-of-the-art TSFMs for zero-shot forecasting tasks. Overall, this work establishes a strong foundation for retrieval-augmented time series modeling.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1.\tThis paper adapts the retrieval-augmented generation paradigm from NLP to time series by incorporating an Adaptive Retrieval Mixer module that can dynamically fuse retrieved historical patterns with the query, and enhance model generalization without task-specific fine-tuning. This design fills a critical gap in current TSFM literature where adaptation and external knowledge incorporation remain limited.\n2.\tThis paper is well-written, and the empirical results are quite solid. Extensive zero-shot time series forecasting experiments have been conducted over 7 diverse public benchmark datasets under the various retrieval knowledge base settings of in-domain, cross-domain, and multi-domain. TS-RAG shows consistent improvements in MSE and MAE across all datasets, with different TSFMs and settings.\n3.\tAblation studies on module design choices and retrieval configurations are provided, and the results are convincing to validate the effectiveness and robustness of the TS-RAG model design. Case studies are also provided to illustrate how retrieved sequences align and work with input queries, providing explainability/insight for model reasoning.\n4.\tCode has been provided and properly documented to improve the reproducibility.\n\nWeaknesses:\n1.\tIt would be better to provide more explanations for the results in Figure 2, knowledge bases (right).\n2.\tSome implementation details, such as hyperparameter choices for ARM (e.g., MHA dimension) and retrieval database construction strategies, are provided in the appendices. It would be better to provide selected key information in the main text to improve clarity.\n\nQUESTIONS:\nQ1: Please provide more explanations for the results in Figure 2, knowledge bases (right).\nQ2: While TS-RAG effectively retrieves time series segments to enhance zero-shot forecasting performance of TSFMs, I wonder whether incorporating external modalities (e.g., text or structured metadata) could further enhance the forecasting performance. (I understand this may not be the focus of this paper, but providing some insights or discussions would be helpful.)\n\nLIMITATIONS:\nSee weakness.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-04"}