TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Authors: Kanghui Ning
Zijie Pan
Yu Liu
Yushan Jiang
James Y. Zhang
Kashif Rasul
Anderson Schneider
Lintao Ma
Yuriy Nevmyvaka
Dongjin Song
Venue: NeurIPS 2025 poster ()
Keywords: Time Series Forecasting; Foundation Model; Retrieval-Augmented Generation
Primary area: deep_learning

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.

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning'
Zijie Pan
Yu Liu
Yushan Jiang'
James Yiming Zhang
Kashif Rasul Yuriy Nevmyvaka** Dongjin Song!
Anderson Schneider
Lintao Ma
SScool of Computing. University of Connecticut, Storrs, USA.
2 DDpartment of Machine Learning Research, Morgan Stanley, New York, USA.
3 Ant Group, Hangzhou, China. *TWG Global, New York, USA.
Abstract
Large Language Models (LLMs) and Foundation Models (FMs) have recently be- come prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they often struggle 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 -augeeted 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 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 seven public benchmark datasets demonstrate that TS- TS-RAG achieves sfate-of-the-art zero-shot forecasting perfor- mance, outperforming the existing TSFMs by up 6.84% across diverse domains while also providing desirable interpretability. Our code and data are available at: https://gi thub. com Sonm/UUCnn-DSSS/TSS...
Introduction
Time series forecasting, which aims to predict future values of a sequence based on its past ob- servations, plays critical role in various real-world applications, e.g., finance [1], healthcare (2]. energy management [3], and climate science [.. Modeling time series data essentially captures the temporal dependency patterns in the form of trend, seasonality, autocorrelation, elc, make accurate predictions and generalize across different datasets. In the past, a substantial amount of effort has been made tackle this problem. Traditional statistical methods such as AutoRegressive Integrated Moving Average (ARIMA) (5) work well for stationary time series but struggle with complex de- pendencies and non-linear patterns. Machine learning approaches, such as Random Forest (6] and XGBoost [7]. can handle external covariates of features but fail to capture long-range dependencies. Deep learning techniques, including Long Short-Term Memory (LSTM) [ Gated Recurrent Units (GRUs) 9 Temporal Convolutional Networks (TCNs) [10]. Graph Neural Networks (GNN) based models 11 12 and transformer based models B
14 are typically trained within specific domains
'Correspondence to: Lintao Ma <lintao.mlte anmm, Yuriy Nevmyvaka <yuriy. @erryvknemmy morganstanley.coms, Dongjin Song <dongjin song @ uconn.edux
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Trainable
M
Frozen
Time Series Forecast
Linear
Augment
general framework of Foundation Models
Return
Output Projection
MLP
Retrieved Knowledge
Next Token Prediction
GPT
Ts
Retrieval- Aetrieval-uummented Generation
Backbone 8
LLaMA
RNN
Retrieval Knowledge Base
Embedder
CNN
MLP
Encoder E
Patch
Retrieve
Instance Norm
Query
Inppu Time Series
Retriever
Figure 1: Overview of the proposed IS-RAG framework. Given input time series the query, the retriever accesses a knowledge base to obtain semantically related information. The retrieved knowledge is subsequently integrated into frozen time series foundation model (which can adopt various architectures and design choices) to enhance forecasting performance.
and often struggle to generalize effectively to diverse, unseen datasets. More recently, there is a prevalent interest in adapting Large Language Models (LLMs) [3 16
for time series
tasks [1] [0) and developing Foundation Models (FM) tailored for time series data 21 22 23 24]. Although both LLMs and FM have shown great promise improving forecasting accuracy and handling complex temporal dynamics, they still face immense barriers when applied to zero-shot forecasting tasks, limiting their real-world applicability.
Specifically, recent work (25) has explored the potential usage of LLMs as zero-shot forecasters for time series tasks. While fine-tuning LLMs can facilitate adaptation and yield better performance over specific datasets, such methods often struggle to generalize across diverse, unseen domains [26] 27 [28], and typically involve substantial computational overhead, even when applied to limited
have emerged as a promising alternative by learning time series representations across a wide range of time series data. However, they lack inherent mechanisms for domain adaptation, as they cannot incorporate external contextual knowledge dynamically. This shortcoming limits their capability in handling complex, non-stationary, and evolving time series patterns. Furthermore, most TSFMs offer limited interpretability, which poses challenges for their deployment in high-stakes or decision-critical applications.
data. Time Series Foundation Models (TSFMs) (29 30
31 32
Recently, Retrieval- Augmented Generation (RAG) 33 has demonstrated significant success across various Natural Language Processing (NLP) tasks (3 35 36 37.. By retrieving relevant document segments and incorporating them into prompts, RAG refines the existing prompts and enables LLMs to generate more informed, context-aware outputs, thereby improving both accuracy and adaptability in diverse applications. Motivated by this paradigm, we propose TS-RAG, retrieval- augmented generation based time series forecasting framework. TS-RAG dynamically retrieves semantically relevant time series patterns and integrates them into the forecasting pipeline, enabling strong zero-shot performance without the need for fine-tuning. In addition, significantly enhances the interpretability of TSFMs by providing contextual evidence for their predictions. An overview of the proposed framework illustrated in Figure [ where retriever queries a knowledge base for semantically related time-series information, and the retrieved knowledge is used to augment frozen TSFM that can adopt diverse architectural designs.
Instead of simply relying on the input time series query. TS- RS-RAG first adopts pre-trained time series encoders to retrieve relevant time series segments from a dedicated knowledge database, providing valuable contextual knowledge for forecasting. Next, to effectively integrate retrieved time series knowledge, TS-RAG leverages a learnable Adaptive Retrieval Mixer (ARM) augmentation module, which can dynamically fuse retrieved patterns with the input time series query, ensuring that the model benefits from both existing knowledge and the current query. With aatmenel generation, TS-RAG not only circumvent the need for fine-tuning specific datasets but also can

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utilize retrieved segments to provide explicit rationales to enhance the interpretability of the model's predictions. Finally, thorough empirical studies on seven public benchmark datasets demonstrate that TS-RAG achieves state-of-the-art zero-shot forecasting performance, outprforming existing TSFMs by up to 6.84% across diverse domains while exhibiting desirable interpretability, highlighting its potential as robust and generalizable forecasting framework.
2 Related Work
Time Series Foundation Models Existing LLM-based time series forecasters 226
38
39
27
40
28 41] have demonstrated remarkable achievements in in-domain time series analysis. However, the domain adaptation challenges and significant computational costs associated with LLM-based models have motivated the emergence of time series foundation models as a more efficient and scalable alternative. Inspired by recent advancements in Natural Language Processing (NLP) and Vision Transformers [42 Time Series Foundation Models have rapidly developed and drawn significant attention. These models have demonstrated strong generalization capabilities across diverse datasets, leading to substantial progress in time series forecasting. Lag-Llama [21] and TimeGPT-1 [22] are pioneering forecasting foundation models, pre-trained on extensive time series datasets spanning multiple domains. Lag-Llama utilizes lagged time series features and the LLaMA architecture [43], while TimeGPT-1 adopts an encoder-decoder transformer structure to handle forecasting tasks effectively. Following the paradigm of patching tokenization and optimization, models such as TimesFM [29]. MOMENT 44, Timer 45 46), and Sundial [47] first patch and embed continuous time series values, and subsequently model the output distributions and perform point forecasting. To further improve efficiency, Tiny Time Mixers (TTMs) 30) train compact foundation model, while TimeMOEE[8) adopts a sparse mixture-of networks during inference, thereby maintaining strong performance with reduced computational cost. However, deterministic predictions usually cannot satisfy the requirement of decision-making address this limitation, Moirai 32 trains probabilistic model that captures mixture of distributions. Building on language modeling techniques, Chronos [31 discretizes time series through scaling and quantization, and models the resulting categorical distributions using cross-entropy loss. To enhance inference efficiency and forecasting accuracy, Chronos- -Bolt replaces discrete tokenization with a patch-based input strategy and utilizes decoder representations to produce quantile forecasts over multiple future steps, achieving improved performance over its predecessor. These methods, however, lack inherent mechanisms to incorporate external contextual knowledge dynamically to facilitate zero-shot learning and suffer from limited interpretability.
architecture that activates only a subset of expert
To
Eetrieval-Augmennte for Time Series Forecasting Although both LLMs and TSFMs have achieved strong performance in time series forecasting, they can still struggle in scenarios involving non- stationarity or distribution shifts. To address these challenges, Retrieval-Augmennt (RAG) techniques (33) offer a promising approach to incorporate external knowledge and enhance generalization and robustness. Recent works have explored the integration of retrieval mechanisms into time series forecasting. Among them, several approaches rely on fine-tuning to adapt the model to downstream tasks, including ReTime 49, RATD 50 TimeRAG [51] and RAFT (52). ReTime [49 proposes relational retrieval and content synthesis for retrieval- -ased time series fore- casting. RATD (50) utilizes retrieved historical time series to guide the denoising process of diffusion models, enhancing forecasting performance. RAFT [52) integrates retrieval with a multi-resolution forecasting framework. These methods typically construct the retrieval database from the training set of the target task and require fine-tuning for model adaptation. Similarly, TimeRAG [51] incorporates retrieved sequences into the forecasting process by using a frozen LLM backbone, and introduces a trainable reprogramming layer to align time series and text modalities.
Generation
RAF [53] is pioncering work that introduces a retrieval-aue surieval--uugmented framework for zero-shot time series forecasting. It utilizes Chronos as the backbone model and constructs the augmented input by directly concatenating the processed retrieved context with the original time series input. However, this approach may face scalability and efficiency challenges when applied to large-scale retrieval databases. Moreover, RAG techniques for time series forecasting remain underexplored, particularly in terms of knowledge base construction, augmentation strategies, and their integration with TSFMs to facilitate zero-shot forecasting. To bridge these gaps, we propose TS-RAG, a retrieval-augmenntd framework specifically designed to enhance zero-shot forecasting capabilities of TSFMs.
3

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Trainable
Frozen
Retrieval Eucoder
Retrival Knowledge
MHA
Base
FEN & Dropout
Sticing
Embedding
Slicing
Paired Future
Weighting
Retriever
Retrieved
Mixing
Input TS
(Emheeding Similarity Ranking)
TS pairs
Embedding
Projector
Final TS Forecast
TSFM Backhone
TSFM
ARM
Output Layer
Concat. Embedding
Figure 2: The TS-RAG model architecture processes an input time series by retrieving the top-k semantically similar time series segments and their corresponding future horizons from a knowledge base (via Retriever), based on embedding similarity. These retrieved segments are then integrated with the input series embedding using the proposed Adaptive Retrieval Mixer (ARM) augmentation module, enabling the model generate the final forecast with enriched contextual information.
TS-RAG for Zero-Shot Time Series Forecasting
The proposed TS-RAG consists of three key components, i.e., a time series foundation model, retriever, and learnable Adaptive Retrieval Mixer (ARM) augmentation module, as shown in Figure 2 Given an input time series, a pretrained retriever encoder first generates the corresponding embedding. This embedding then compared with time series context embeddings previously stored in the retrieval knowledge base to retrieve the top-k similar time series pairs. Each retrieved pair includes historical context and its corresponding forecasting horizon, and the forecasting horizon is utilized for augmentation to refine the zero-shot time series forecasting.
Overview:
The retrieved future horizons of top-k similar time series pairs first transformed into embeddings and then fed into the ARM augmentation module along with the input time series embedding generated by the TSFM backbone. The ARM augmentation module adaptively assigns importance scores to these embeddings, dynamically integrating them into a unified representation. This final representation is then passed through the output projection layer of the TSFM produce the enhanced time series forecast. Below, we provide details for the construction of the retrieval knowledge base (Section 3.I), the TS-RAG framework architecture (Section 3.2). and the pretraining strategy used to enable zero-shot inference (Section 3.3)
3.1 Construction of the Retrieval Knowledge Base
TSFMs are typically pretrained multi-domain time series dataset enhance their generalization capability. For TS-RAG, we adopt a similar strategy, i.e., construct a multi-domain dataset and specifically focus on learning the ARM augmentation module. We leverage the pretraining dataset of Chronos (31), which utilizes TSMixup to randomly combine time series data points from various domains. This approach enhances data diversity by blending different patterns, thereby improving the model's ability to generalize. Given the fact that the trainable parameters of TS-RAG are significantly less than those in the TSFM backbone and the retrieval encoder (more discussions are available in Appendix B.4]. we uniformly sample a subset from the Chronos pretraining dataset to serve as the pretraining dataset for TS-RAG. Based this subset, retrieval knowledge base for TS-RAG, which will be used in the inference (forecasting) stage.
further draw subset to construct the
The time series data stored in the knowledge base is processed into standard pairs, each consisting of a context window and its corresponding forecasting horizon. Formally, this can be expressed as: {xx, y:) j =1,2,.. ......., 1,,...., n1 where x; E _ is the context window of the i-th time series with length

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T,y; E R is the future horizon of length L associated with x,, and n is the total number of pairs in the knowledge base. Based on the time series context windows {x,] 1.2.....) nn stored in the retrieval knowledge base, we employ pretrained retrieval encoder to generate their embeddings {e:] i = .,2....7) where e; E Rdd These embeddings are then stored along with the corresponding
time series data in the knowledge base. As result, the structure of the retrieval knowledge base can
be formally described as the following set of triplets:
D = {(x,, y.), li= = -.22.....77
where D represents the retrieval knowledge base, and e; denotes the embedding of the input sequence X,, computed by pretrained retrieval encoder, thereby facilitating efficient training process.
3.2 Architecture of TS-RAG Framework
By leveraging relevant time series contexts retrieved from an external knowledge database, TS-RAG can enrich the input query time series with additional contextual information, thereby improving both model generalization ability and forecasting accuracy.
Within TS-RAG, TSFM has three key components
[54] encoding layer, which may include
normalization and embedding layers to preprocess and transform the input time series; a backbone,
typically implemented as transformer based model (i.e. GPT (55). T5 [56]. Llama [T6] etc.) to extract temporal representations; and a projection layer, often implemented as a multi-layer perceptron (MLP), which maps the temporal representations from the backbone the final prediction values.
TS-RAG introduces two additional components: a Retriever and an Adaptive Retrieval Mixer (ARM) augmentation module. These components work alongside the TSFM backbone, enabling the model to adaptively integrate retrieved information and improve forecasting accuracy. More specifically, the encoder from the pretrained retrieval encoder (e.g.. Chronos encoder) is used the Retriever Encoder, which generates embeddings for both the query time series and the time series contexts stored in the retrieval knowledge database. The Retriever calculates the Fuclidean distance between the query embedding and each stored context embedding in the knowledge base, and then selects the top-k similar candidates based on the smallest distance.
Retriever. Formally, given a query context Xg. we first obtain its embedding using the retrieval encoder fenc:
e Senc (x²)-
Next, the Euclidean distance between the query embedding and each stored embedding in the retrieval knowledge base is calculated:
d(e,, e, = lle, - ez
Vi (1,
To identify the most relevant time series patterns, the retrieval mechanism selects the top-k candidates with the smallest distance:
TopK yi, e:)) 1,,i:,,,,..,,,......
TopKmin (.) returns the top-k entries ranked by the smallest distance values d(e,, e,). The retrieved set contains the most relevant context-forecast pairs, which are subsequently used augment the forecasting process.
Adaptive Retrieval Mixer (ARM). To perform forecasting, we develop a novel ARM augmentation module to integrate the projections of the top-k retrieved forecasting horizons with the query time series embedding from the TSFM backbone to enhance prediction accuracy. Each embedding is dynamically weighted by the ARM module and contributes accordingly to the final forecast. Initially, each retrieved forecasting horizon y; is encoded independently using a learnable projector:
fMiP(y;), i= 1,2,....
(5)
where fMLP is feedforward network that maps each retrieved sequence into dense representation of a d'dimensional vector. The resulting embeddings are stacked along a new dimension, forming:
Exat = (ē,, êg, ..... ēx} E R kxd
(6)
5

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where d is the embedding dimension. To fuse the retrieved information with the query time series representation é, R 1xd generated by the TSFM backbone, the two are concatenated into a single representation:
Econaat [ Eaerl E (
(7)
This combined representation is then passed through a Multi-Head Attention (MHA) layer with residual connection learn interactions between all the embeddings:
a =
MHA (HA(Eecmcal) + Econcats
(8)
where Ean E 1)x++ represents the contextualized features.
Next, we apply feed-forward network (FFN) with dropout to further transform these contextualized features, followed by a residual connection to preserve the original information:
Emn Dropout (FFN + Emn
mixing mechanism is then applied adaptively balance the contributions of the retrieved sequence representations and the model's original representation. Specifically, scoring network computes an importance weight for each representation:
= Softmax W, Emi + bg).
(10)
where W, y and 6, are learnable parameters, and a E [x++1)x1 denotes the normalized attention weights. The mixed representation is computed as a weighted sum, while a skip connection is applied to preserve the information from the TSFM's pretrained modeling capability:
k+1
etnaal
éq
+
Σ
a
Ema.
(11)
2=1
Finally, the enriched sequence output Gfinal passed through the output projection layer of TSFM to generate the final forecast:
yq = Spnjf (Cfiml);
(12)
and we follow the same training objective the TSFM backbone (see Setup in Section 4.1
The ARM mechanism enhances forecasting in several key aspects. By leveraging retrieved sequences. the model gains access to additional information, which is particularly valuable when the query context alone insufficient for accurate predictions. The Multi-Head Attention mechanism enables the model to learn context-aware interactions between the retrieved data and its predictions. Next, the mixing mechanism adaptively determines the importance of each candidate, allowing the model to focus on the most relevant information. Finally, the skip connection ensures that the model's s initial predictions are preserved and enriched, maintaining a balance between query knowledge and external augmentation. These designs collectively improve the prediction accuracy and enhance the interpretability of the model, particularly in scro-shot forecasting scenarios.
3.3 Pretraining Strategy and Zero-shot Inference
Pretraining Strategy. For pretraining, we selectively only train the external parameters of the projector and the ARM augmentation module in TS-RAG based on pre-constructed multi-domain datasets, while keeping all other parameters (the TSFM backbone and the Retrieval Encoder) frozen.
Zero-shot Inference. During the zero-shot inference stage, TS-RAG utilizes its pretrained compo- nents to generate forecasts without any task-specific fine-tuning- The RAG approach enables TS-RAG to generalize across diverse forecasting tasks by leveraging external knowledge from broad set of time series domains. Our experiments in Section 4 demonstrate the effectiveness of TS-RAG in in-domain, distribution shift, cross-domain, and multi-domain settings.
Experiments
4.1 Experimental Setup
Datasets and Retrieval Knowledge Base. For the pretraining dataset, we first uniformly sample 50 million data points from the Chronos pretraining dataset [31] and further uniformly sample a subset

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Table 1: Long-term zero-shot forecasting results. Best results are highlighted in bold, and second best results are underlined. " "-" indicates the datasets were used in pretraining and zero-shot results are not reported. More results are in Appendix B.2 Table 6
Methods
Chronos-bollg
MOMENT
TTM,
Mairalz
TimesFM
Chronceg
Metric
MSF
MAF
MSF
MAF
MSE
MAR
MSE
MAR
MSR
MAE
MSE
MAR
MSE
MAR
ETTh1
0.3557
0.3624
0.3616
0.350
0.3920
0,4110
0,3619
0.3710
0.3686
0.3835
0.4254
0.3825
0:4217
0.3806
ETTh2
0.2451
0.2982
0.2517
0.2992
0.2742
0.3327
0.2531
0.3032
0.2547
0.3053
0,2894
0,3233
0,2659
0.3136
ETTml
0.2906
0.3109
0.3185
0.3506
0,3834
0.3248
0.5399
0.3321
0.3326
0.3935
0,3695
FTTm2
0.1466
0.2231
0.1487
0.2236
0. 0.1703
0.2579
0.1511
0.2405
0,1958
0.2667
0.1703
0,2552
0.1663
0,2522
Weather
0.1454
0.1771
0.1525
0.1825
0.1801
0,2384
0.1543
0.1893
0.1711
0.1912
0.1897
0.2107
Electncity
0.1120
0.2002
0.1132
0.2004
0.1967
0,3028
0.1715
0.2643
0.2237
Exchange
0.0627
0.1718
0.0673
0.1780
0.0979
0,2069
0.0657
0.1725
0,0663
0.1720
0.0695
0.1802
0.0831
0.1879
of 5 million data points to construct the multi-domain retrieval knowledge base. To facilitate efficient indexing and retrieval, both the pretraining dataset and the retrieval knowledge base are segmented using a predefined context window. This process results in a total of 26 million pretraining data pairs and 2.8 million retrieval knowledge base pairs.
The zero-shot experiments are conducted on widely recognized time series benchmark datasets spanning diverse domains, including ETTh1, ETTh2, ETTm ETTm2, Weather, Electricity, and Exchange Rate. Details of these datasets can be found in Appendix AA.4. Zero-shot evaluation is performed on the test sets of these datasets, with a data split ratio of 6:2:2 for the ETT datasets and 7:1:2 for Weather, Electricity, and Exchange Rate.
During zero-shot inference, the retrieval knowledge base can be constructed in various ways, including in-domain, distribution shift, cross-domain, and multi-domain settings. We further discuss the setup and impact of different knowledge base choices in Section 4.3
Baselines. In practice, we use Chronos-Bol Bolt, one of the state-of-the-ar TSFMs, as the backbonn of TSTSSAAA it achieves competitive performance in our evaluations. TS-RAG is designed to be compatible with any general TSFM. While our main experiments primarily use Chronos-Boi as the backbone due to its strong empirical performance, we also verify TS-RAG's effectiveness with MOMENT 44] in Appendix B.I For comparison, we also report the zero-shot performance of
other TSFMs, including TTM 30 TimesFM (29). Moirai 32) Chronos 31 Chronos-Boo (31]
MOMENT and Time-MoE
48
Setup. Given that TSFMs are typically trained with a fixed forecasting length (e.g., 64 or 96), we maintain this consistency in both pretraining and zero-shot evaluation. The context length is set to 512, and the forecasting length is fixed at 64. We adopt the same forecasting loss as the backbone TSFM; when using Chronos- Bollt as the backbone, we apply the quantile regression loss following its original implementation. Mean Squared Error (MSE) and Mean Absolute Error (MAE) are used as primary evaluation metrics, with detailed definitions provided in Appendix AA.5
4.2 Experimental Results for Zero-shot Forecasting
As shown in Table TS-RAGCChonss-Bod consistently outperforms other TSFMs, including its back- bone Chronos-Boo Bolt, across all datasets, demonstrating its effectivvness in leveraging external patterns to enhance zero-shot forecasting.
Compared to Chronos-Boll TS- TS-RAGGinmm- Bolt achieves an average reduction of 3.54% in MSE and 1.43% in MAE, confirming that the incorporation of retrieved information improves both precision and robustness. Notably, Chronos-Bolt already performs well on the Exchange Rate dataset, achieving an MSE of 0.0673, yet ASSRRAChhoons-Bed further reduces the MSE by 6.84%, demonstrating its ability to retine forecasts even when the backbone is highly optimized.
Across each individual dataset, TS-RAGChnnss--E Bool consistently achieves the lowest MSE and MAE, demonstrating its robustness across diverse time series patterns. Significant performance gains are observed on datasets such as ETTm] and Weather, where TS-RAG not only outperforms Chronos- Bolt but also surpasses all other TSFMs by a notable margin. This improvement suggests that RAG is particularly effective in datasets with complex temporal dependencies, where incorporating relevant time series patterns from existing database significantly enhances forecasting accuracy.
7

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Avg. MISE
Avg. MAE
Arg. MES
Arg. MAE
0.198
0.302
W/O BAG
WY Clads
W. AHIM
0.252
0.250
0.270
0.266
0.268
Doleiiurionn Staft
Crur-Bvertinn
Barelie
Moat--Dmaan
Uvenain
0.3050
0.3015
0.196
0.2975
0.262
0.194
0.2950
0.258
0.190
0.256
0.2900
Figure 3: Comparison of average MSE and MAE across augmentation methods (left) and knowledge bases (right). w/o RAG indicates Chronos-Boo Bolt baseline; w/ ARM and w/ Gate refer to TS-RAG with ARM and Gate augmentation modules, respectively. More detailed results are in Appendix B.2
4.3 Ablation Studies
Effectiveness of ARM Module The augmentation module plays critical role in TS-RAG, as determines how the retrieved forecasting horizons are integrated with the query representation. An ineffective fusion design could limit the benefits of retrieval, leading sub- sub-optimal forecasting performance. To assess the contribution of the ARM module, we conduct ablation experiments where we replace the ARM with simpler alternative: gated fusion module that directly linearly combines the TSFM's forecast with the retrieved forecasting horizon.
We compare the two augmentation modules under the same pretrain-zeroshe setting, keeping all other components fixed. Full results are in Appendix B.2 Table 8 As shown in Figure ((eut)(() (left), although the gated fusion variant ("W/Gate") also brings performance improvements over the TSFM baseline, the gains are consistently lower than those achieved by the ARM-based TS- TS-RAG ("W/ARM"). This result highlights the effectiveness of the ARM module in dynamically mixing the contributions of different retrieved patterns as well as the original output representation. The iitention-basse fusion with residual connection in ARM enables richer interactions and adaptive integration, which proves to be crucial for zero-shot forecasting.
Impact of Retrieval Knowledge Base We evaluate the impact four retrieval knowledge base configurations on zero-shot forecasting performance, as shown in Figure 3 (right). The configurations include: (1) in-domain, the retrieval knowledge base is built using the training set of the same dataset; (2) distribution shift, the retrieval knowledge base is constructed using the training set from a closely related dataset with different distribution (e.g., using the ETTh2 training set when evaluating on ETTh1); (3) cross-domane
using different domain data; and (4) multi-domant using data from multiple domains (as mentioned in the experimental setup, we construct the multi-domain knowledge base using subsets of the pretraining dataset).
The detailed results are in Appendix B.2 Table 7
Across all configurations, TS-RAG improves forecasting performance over the baseline, particularly in MSE, where in-domain retrieval consistently achieves the lowest error. These results suggest that while retrieval generally enhances forecasting, the composition of the retrieval knowledge base plays a crucial role.
Effectiveness of Longer Forecasting Horizons Time Series Foundation Models (TSFMs) typically employ a rolling strategy when forecasting a horizon longer than their pretraining length. In this approach, the model iteratively generates predictions for shorter segments and then rolls forward to forecast the next segment until the full horizon is covered. TS-RAG follows similar strategy but enhances with retrieval augmentation. Specifically, for each forecasting step, TS-RAG retrieves next 64-step forecasting horizon from the retrieval knowledge base, incorporating relevant historical patterns each iteration until the specified forecasting length reached.
Table 2 presents the zero-shot forecasting results across multiple datasets. including ETTh, ETTm. Weather, Electricity, and Exchange Rate. The results show that TS-RAG consistently outperforms its backbone model, demonstrating the effectiveness of RAG in extending prediction horizons while maintaining accuracy. The performance gain suggests that leveraging retrieved sequences mitigates error accumulation, a common issue in rolling- based forecasting.
Effect of Retrieval Lookback Length Table 3 presents the effect of different retrieval lookback lengths zero-shot forecasting performance. Given an input sequence of length 512, we explore
8

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Table 2: Zero-shot forecasting results for extended forecasting horizons across multiple datasets. We report MSE. More results are in Appendix B.2 Table
Forecasting Length
96
192
336
720
Methods
w/o
TS-RAG
TS-RAG
RAG
TS-RAG
w/o
TS-RAG
ETTh1
0,3859
0.3772
0.4446
0.4306
0.4850
0.4650
0-4841
0.4703
0.2899
0.3603
0.3474
0.4045
0.3839
Eloctricity
0.1242
0.1226
0.1428
0.1413
0,1613
0,1593
0,2069
0.2050
Exchange rale
0.0993
0.0927
0.1926
0.1831
0.3437
0.3157
0.8100
0.6968
Table 3: Long-term zero-shot forecasting results with different retrieval lookback lengths. The best results highlighted in bold, and the second-best results underlined. MSE is reported here.
Loukback Length
Metric
ETTh
ETTml
ETTm2
Weather
Electricity
Exchange
Average
64
MSE
0.3540
0,2432
0,3114
0.1502
0,1491
0.1132
0.0678
0.1984
128
MSE
0.3572
0,2415
0,2935
0.1494
0,1526
0,1
0.0674
0.1963
MSF
0.2409
0.3195
0.1518
0.1130
0.1996
512
MSE
0.3557
0.2451
0.2906
0.1466
0.1454
0.1120
0.0627
0.1940
different retrieval configurations by using only the last 64, 128 or 256 time steps, or the full 512 time steps for retrieval. Across all datasets, longer retrieval lookback windows (256 or 512) yield relatively better performance, suggesting that incorporating a more extended historical context helps retrieve more relevant sequences. This finding demonstrates that retrieving from longer historical sequences generally improves the quality of retrieved sequences, leading to greater forecasting accuracy. However, in some cases, longer is not always better, indicating that excessive retrieval windows may introduce noise or irrelevant information. This suggests the potential for adaptive retrieval mechanisms that allow the retriever to dynamically determine the most suitable retrieval lookback length for each instance.
Additional Ablation Study We further conduct additional ablation studies to investigate the impact of retriever configurations, including the number of retrieved sequences, retriever encoder choice, and retrieval distance metrics. The results confirm that while TS-RAG benefits from moderate number of retrieved sequences, it is robust to the choice of retriever encoder and retrieval distance metric. Detailed results and analysis are provided in Appendices B.3 B.4 and B.5
4.4 Comparison with RAF
To further evaluate the performance of TS-RAG, we compare with RAF (Retrieval Augmented Forecasting) (53) recent retrieval-augmented method for zero-shot time series forecasting. As shown in Table we conduct the comparison from two aspects: effectiveness and efficiency. More implementation details can be found in Appendix A.3
Effectiveness We evaluate the zero-shot forecasting performance of TS-RAG and RAF seven benchmark datasets. TS-RAG achieves an average MSE of 0. 0940, outperforming RAF, which records an average MSE of 0.2320. Moreover, TS-RAG consistently delivers lower errors across all datasets, highlighting its superior generalization capability and robustness.
Efficiency We also compare the inference speed of TS-RAG and RAF on the ETTh dataset. TS- RAG significantly outperforms RAF in terms of inference efficiency. Specifically, for the retrieval stage. TS-RAG completes each iteration in just 9.2 ms, whereas RAF requires 3290 ms per iteration. This substantial speed improvement mainly comes from two factors: (1) the use of FAISS for fast nearest-neighte search, and (2) TS-RAG conducts retrieval over compact embeddings.
4.5 Interpretable Forecasting with Case Studies
TSFMs are often used as black boxes, making it difficult to understand how predictions are made. TS-RAG addresses this by providing two key interpretability features: (1) setrivall-ssevvddnee which surfaces top-k analogue sequences for each query window, and (2) transparent weighting,

---

Table 4: Zero-shot forecasting comparison between TS-RAG and RAF. (Left) Average MSE and MAE for 512-64 forecasting across seven benchmarks. (Right) Average Inference Speed ETTh datasets. More results are Appendix
[B.6] [
Method
Averuge MSE
Average MAE
Method
Retrieval
Forward Pass
Total
RAF
0.2318
0.2738
RAF
3290 mi/iter
184 mifiter
3474 ma/iter
TS RAG (Ours)
0.1940
0.2492
TS-RAG (Ows)
9.2 ms/iter
0.44 ms/iter
9.62 ms/iter
(mmput) (BroundTruti)
IG
evedd
Top-1 Retrivved seq Top-2 Rechieved seq Top-3 Rerrieved
[Preediced]
y /FFrecictent)
Figure 4: Case study TS-RAG retrieval (Weather): Given the query time series, the re- triever selects relevant historical sequences based on the embedding of the query. The retrieved sequences exhibit strong similarity to the input query in terms of both trend and periodicity.
RAG
Figure 5: Case study on TS-RAG retrieval and forecasting (ETTm1): Given the retrieved se- quence, the forecasting result with RAG better aligns with the sharp downward trend.
which highlights the most relevant subset of retrieved sequences that have the greatest influence through similarity scores.
We illustrate these capabilities with case studies on retrieval quality and its impact on forecasting performance. Figure 4 shows the retrieval process on the Weather dataset, where the retriever selects highly relevant sequences aligned with both trend and periodicity of the query, serving as explicit evidence for the forecast. Figure presents case from the ETTml dataset, where TS-RAG improves forecast accuracy by leveraging retrieved sequences that exhibit similar sharp downward trends, which the backbone TSFM fails capture. The retrieved forecasting horizon shown corresponds to the retrieved sequence with the highest weighting, highlighting the model's ability to transparently surface and utilize the most influential retrieved patterns.
These examples demonstrate that TS-RAG not only improves accuracy but also makes its forecasts more interpretable by exposing the key retrieved patterns and their contributions. More case studies are provided in Appendix
5 Conclusion
In this paper, we introduced TS-RAG, a novel eetrieval-augmented forecasting framework designed to enhance the generalization and interpretability of Time Series Foundation Models (TSFMs) in zero-shot forecasting. By integrating retrieval-augmented generation (RAG) with pretrained retrieval encoder and an Adaptive Retrieval Mixer (ARM) augmentation module, TS-RAG effectively incorporates retrieved relevant patterns to improve forecasting accuracy in previously unseen domains. Extensive empirical evaluations on multiple benchmark datasets demonstrate that TS-RAG can consistently enhance the zero-shot forecasting performance of various TSFMs across diverse domains. Furthermore, we systematically explore the impact of different retrieval configurations, validating TS-RAG as general and flexible framework for retrieval-auge augmented time series forecasting.
In summary, TS-RAG establishes strong foundation for retrieval -agmmetee time series forecasting, setting up new frontier for robust and adaptable time series forecasting in dynamic and open world environments. Looking ahead, we aim to 1) explore multimodal extensions of TS-RAG by integrating heterogeneous time series data, such as text data, to further enhance forecasting capabilities; 2) investigate optimization techniques for retrieval ranking in RAG, assessing whether more effective retrieval mechanisms can further boost zero-shot forecasting performance.
10

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Acknowledgements
The authors gratefully acknowledge the support from Morgan Stanley, Part of this work was conducted during Kanghui Ning's 's internship at Ant Group.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly present the key contributions of TS- RAG and these claims are substantiated by clear explainations in methodology section and extensive experimental results across seven public benchmarks, with clear performance metrics supporting the stated improvements.
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The answer NA means that the abstract and introduction do not include the claims made the paper.
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The paper discusses several limitations implicitly throughout the ablation stud- ies and explicitly mentions in Section 4.3.3 that in- in-domain retrieval generally performs best, indicating the limitation on data perspectives. Future directions also reflect acknowledgment of current limitations, including the exploration of hybrid or multimodal retrieval and better retrieval strategies. We also include the limitation section in the Appendix.
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A Experimental Details
A.1 TS-RAG Implementation Details
During pretraining. all parameters of the TSFM backbone are frozen, and only the additional parameters introduced by TS-RAG are fine-tuned. The same forecasting loss as the backbone TSFM adopted; specifically, when using Chronos-Bolt the backhone, we apply the quantile regression loss following its original implementation. The number of retrieved sequences (top-k) is set to 10 by default; however, due to the flexible design of the ARM augmentation module, different values of can be explored. The model is trained using the AdamW optimizer with a learning rate of 0.0003 and weight decay of 0.01. The batch size set to 256, and training is conducted for 10,000 steps. To improve generalization, dropout is applied to certain layers with a dropout rate of 0.2. The training process was conducted on NVIDIA A6000-48G GPU using TF32 precision. For efficient retrieval, FAISS is used to quickly identify the most relevant sequences from the retrieval knowledge base.
A.2
TSFM Baseline Introduction
We introduce the baseline models that we choose to compare in the following section:
. Chronos-Bolt [31 Chronos-Bole is subsequent version of Chronos, which can handle patch-based and uses decoder representations to generate quantile forecasts across multiple future steps, improving forecast accuracy over Chronos.
MOMENT 44]:
MOMENT uses masking modeling technique for zero-shot forecasting by appending a lookback series with a mask that matches the length of the forecast. involves pretraining a Transformer encoder model univariately on the "Time Series Pile" datasets, which include a wide variety of time series data.
TTM 30 TTM pre-trains a compact model based on the lightweight TSMixer architecture. It incorporates adaptive patching, diverse resolution sampling, and resolution prefix tuning to pretrain successfully on small dataset.
Moirai [2]: Moirai pretrains the Transformer encoder on the "LOTSA" dataset, which includes 27B time points, by masking the forecast horizon of each target channel and performing mask reconstruction.
TimesFM 29]: TimesFM employs a decoder-style attention model and is pre- pre-trainee -rtiirained in a univariate manner large group of both real-world and synthetic datasets.
Chronos is a probabilistic time series foundation model.Chronee tokenizes the input time series in a quantized manner and processes these tokens using the T5 model [56]. Chronos trained an extensive corpus of collected and synthetic time series data and has great generalization ability.
Chronos 31:
Time-MoE consists of a family of decoder-only time series foundation models with a mixture-of-experts architecture, designed operate in an auto-regressive manner, enabling universal forecasting with arbitrary prediction horizons and long context lengths.
Time-MOE 48
A.3 RAF Implementation
Following the original RAF paper, we implement RAF using the Chronos-Bars model as the backbone. To ensure fair comparison, we align both the test set and the retrieval knowledge base with those used TS-RAG. While the original RAF paper adopts WQL and MASE as evaluation metrics, we report MAE and MSE to maintain consistency with TS-RAG's evaluation protocol.
For the retrieval process, we retrieve the top- most relevant sequences, consistent with the TS-RAG setting. Additionally, we implement an RAF variant using the Chronos-Bolt model as the backbone to fully align model configurations. These adjustments ensure that any observed performance differences come solely from the retrieval and augmentation mechanisms.
22

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A.4 Details of Inference Datasets
We experiment the zero-shot forecasting on the widely adopted Electricity Transformer Temperature
(ETT) datasets [14], Weather, Electricity [57], and Exchange Rate from [58]. ETT datasets are comprised of roughly two years data from two locations in China. The data are further divided into four distinct datasets, each with different sampling rates: ETThl and ETTh2 are sampled hourly, and ETTml and ETTm2 are sampled every 15 minutes. Every ETT dataset includes six power load features and target variable: the oil temperature. The Electricity dataset comprises records of electricity consumption from 321 customers and is measured with a 1-hour sampling rate. The Weather dataset contains one-year records from 21 meteorological stations located in Germany. The sampling rate for the Weather dataset is 10 minutes. The Exchange Rate dataset includes the daily exchange rates of eight foreign countries, including Australia, Britain, Canada, Switzerland, China, Japan, New Zealand, and Singapore, ranging from 1990 to 2016.
A.5 Evaluation Metries
For evaluation metrics, we use the mean square error (MSE) and mean absolute error (MAE) for zero-shot forecasting. We present the calculations of these metries as follows:
MSE = 3 - (Y, (Yx Yn)=
MAE - Σ YA Y 8n)
where H denotes the prediction intervals. Y, and Yn are the h-th ground truth and prediction respectively with h (1,-.., H). For the evaluation metrics in long-term forecasting, clarify that the reported metrics are the normalized versions of MAE/MSE. Although we apply global standardization the data, the information that the scaler used is from training data solely.
A.6 Efficiency Analysis
Training Efficiency TS-RAG designed for efficient adaptation. It freezes the TSFM backbone and trains only lightweight augmentation module (ARM + projection), reducing trainable parameters and improving stability. Retrieval indices are precomputed avoid redundancy. Training on 26M pairs with 20,000 steps takes around hour single NVIDIA A6000 GPU, using TF32 precision and applying dropout.
Inference Efficiency At inference time, TS-RAG introduces a top-k retrieval step. We use FAISS to perform fast nearest-neighhor search over the retrieval knowledge base. On ETTh, retrieval adds 9.2
ms latency per query; the ARM-augmented forward pass adds 0.44 ms. Total inference time is 9.62 ms/query. This overhead is minor and offset by consistent gains (e.g., -6.84% MSE on Exchange rate), making TS-RAG suitable for real-time use. Notably, compared to RAF, which requires 3,47 seconds per query, TS-RAG achieves over x60 faster inference speed, highlighting its superior efficiency and practicality.
B Additional Results
B.1
Generalization across Backbones
To verify the generalization ability TS-S-ACG we further evaluate the MOMENT 44 backbone. in addition to the Chronos-Boo -Bolt results reported in the main text. The same retrieval-anne -agmmented framework is applied without modifying the backbone architecture or training recipes, and all retrieval settings follow those used with Chronos-Boo for fair comparison. Note that to enable the MOMENT model to perform zero-shot long-lerm forecasting, we pretruin a prediction head on the same pretraining data TS-RAG. Table 5 reports the results on both backbones. TS-RAG consistently improves zero-shot forecasting performance regardless of the backbone, demonstrating its plug-and- and-play nature.
23

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Table 5: Zero-shot forecasting results of TS-RAG across different backbones (Chronos-bod and Moment). Both MSE and MAE are reported. The best results are highlighted in bold.
Backbone
Metrie
ETThi
ETTh2
ETTml
ETTm2
Weather Electricity Exchange
Average
MSR
0.3616
0.2517
0.3109
0.1487
0.1525
0,1132
0,0673
0.2008
Chronos-bolt
MAE
0.3650
0.2992
0.3185
0.2236
0.1825
0.2004
0.1780
0.2525
MSE 0.3557
0.2451
0.2906
0.1466
0.1454
0.1120
0.0627
0.1940
TS-RAG Chooosoooobbbooll bolt
MAB
0.3624
0.2982
0.3114
0.2231
0.1771
0.2002
0.1718
0.2492
MSE 0,3920
0.2742
0.3506
0.1703
0.1801
0.1967
0.0979
0.2374
Moment
MAE
0.4110
0.3327
0.3824
0.2579
0.2384
0.3028
0.2059
0.3044
MSD 0.3823
0.2511
0.3325
0.1552
0.1604
0.1920
0.0775
0.2216
IS-RAGMenent
MAE
0.4072
0.3220
0.3738
0.2474
0.2212
0,2994
0.1972
0.2955
Table 6: Full Long-term zero-shot forecasting results across various TSFMs and TS-RAG. Both MSE and MAE are reported. The best results highlighted bold and second-best results underlined.
Backbone
Methods
ETTh1
ETTh2
ETTml
ETTm2
Weather
Electricity
Exchange
Average
MSE
0.3557
0.2451
0.2906
0.1466
0.1454
0.1120
0.0627
0.1940
TS-RAGChhoooo-bot
MAE
0.3624
0.2982
0.3114
0.2231
0.1771
0.2002
0.1718
0.2492
MSE
0.3616
0.2517
0.3109
0.1487
0.1525
0.1132
0.0673
0,2008
Chronos-bolta
MAE
0.3650
0.2992
0.3185
0.2236
0.1525
0,2004
0.1780
0.2525
MSE 0.3920
0.2742
0.3506
0.1703
0.1801
0.1967
0.0979
0.2374
MOMENT
MAE
0.4110
0.3327
0.3824
0.2579
0.2384
0,3028
0.2059
0,3044
MSE
0,3619
0.2531
0.3152
0.1511
0.1543
0.1715
0.0657
0,2104
TTMp
MAE
0,3710
0.3032
0.3248
0.2405
0.1893
0,2643
0.1725
0.2665
MSE
0.3686
0.2547
0.5399
191958
0.1711
0.1832
0.0663
0.2542
Moiraig
MAE
0.3835
0.3053
0.4322
0.2687
0.1912
0.2814
0.1720
0.2906
MSE
0,4254
0.2894
0.3321
0.1703
0.069S
TimesPM
MAE
0,3825
0.3233
0,3326
0,2552
0.1802
MSE 0.4217
0.2659
0.3935
0.1663
0.1897
0.1460
0.0831
0.2380
Chronosg
MAE
0.3806
0.3136
0.3695
0.2522
0.2107
0.2237
0.1879
0.2769
MSE 0,3623
0.2521
0.3213
0. 0.1565
0.1490
0.1137
0.0851
0.2057
Time-MoB
MAE
0,3669
0.3224
0.3340
0.2540
0.1844
0.2026
0.2056
0.2671
B.2 Ablation Study (Extended Results)
In this section, we present the extended results of the ablation studies reported the main text. Due to space constraints, some detailed results were omitted or summarized in the main paper. Here, we provide the complete results to facilitate more comprehensive understanding and reproducibility.
Table Long-tern term zero-shot forecasting results with different retrieval knowledge bases. The best results are highlighted in bold, and the socond-best results underlined.
Knowledge Base
ETTh1
ETTh2
ETTm1
ETTm2
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Cross-Domain
0.3601
0.3667
0.2466
0.2999
0.3046
0.3240
0.1496
0.2279
Distribution-She
0.3586
0.3647
0.2453
0.2996
0.2993
0.3179
0.1502
0.2271
Multi-Domain
0.3564
0.3633
0.2432
0.2973
0.2971
0.3157
0.1513
0.2277
In- In-Domain
0.3557
0.3624
0.2451
0.2982
0.2906
0.3114
0.1466
0.2231
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Table 8: TS-RAG experiment results using different augmentation techniques.
Method
Metric
ETThl
ETTh2
ETTml
ETTm2
Weather
Electricity
Exchange
Average
MSE
0.3557
0.2451
0.2906
0.1466
0.1454
0.1120
0.0627
0.1940
TS-RAGARM
MAE
0,3624
0.2982
0.3114
0.2231
0.1771
0.2002
0.1718
0.2492
MSE
0,3575
0.2498
0,3041
0.1473
0,1501
0.1126
0.0663
0.1982
TS-RAGGsIo
MAE
0.3640
0.2988
0.3154
0.2235
0,1815
0.2005
0.1768
0.2515
MSE 0,3616
0.2517
0,3109
0.1487
0.1525
0.1132
0.0673
0.2008
Chronos-bolt
MAE
0,3650
0.2992
0.3185
0.2236
0.1825
0.2004
0.1780
0.2525
Table 9: Zero-shot forecasting results for extended forecasting horizons (MSE).
Horizon Methods
ETTh1 ETTh2 ETTml
ETTm2
Weather Electricity Exchange
Average
w/o RAG
0.3859
0.2899
0.3323
0.1779
0.1777
0.1242
0.0993
0.2267
96
TS-RAG
0.3772
0.2812
0.3141
0.1753
0.1697
0.1226
0.0927
0.2189
RAG
0.4446
0.3603
0.3838
0.2515
0.2244
0.1428
0.1926
0,2857
192
TS-RAG
0.4306
0.3474
0.3688
0.2462
0.2172
0.1413
0,1831
0.2764
w/o RAG
0.4850
0.4045
0.4374
0.3177
0.2838
0.1613
0,3437
0.3476
336
TS-RAG
0.4650
0.3839
0.4153
0.3115
0.2819
0.1593
0.3157
0.3347
w/o RAG 0.4841
0.4143
0.5285
0.4162
0.3673
0.2069
0.8100
0.4610
720
TS-RAG 0.4703
0.4017
0.4935
0.4164
0.3703
0.2050
0.6968
0.4363
B.3
Sensitivity to the Number of Retrieved Sequences
The impact of varying the number of retrieved sequences (k) on forecasting performance is illustrated in Figure The x-axis represents the number of retrieved sequences, while the y-axis shows the corresponding Mean Squared Error (MSE). Across all datasets, increasing k initially leads to significant decrease in MSE, demonstrating that incorporat- ing additional retrieved sequences helps refine predictions by leveraging retrieved patterns. However, beyond a cer- tain threshold, the improvement plateaus and even decreases slightly in some datasets, indicating diminishing returns as k increases.
ETTh
MSE
0.40
ETTh2
0.37
0.25
ETTmA
0.60
0.40
ETTm2
MSE
Datasct-specific trends further reveal differences in sensi- tivity k. For instance, ETTml and ETTm2 exhibit the most pronounced improvement as increases, with MSE rapidly declining before stabilizing. This suggests that these in-
Weather
Exchange
0.15
datasets benefit significantly from sttrieval-augment ference, likely due strong temporal dependencies in their historical patterns. ETTh1 and ETTh2 show a similar trend but with smaller overall reduction in MSE, indicating that while retrieval beneficial, these datasets may already con- tain strong intrinsic signals, making additional augmentation less impactful. The Weather, Electricity, and Exchange Rate datasets display steady decline MSE with & increas- ing, but the improvement becomes marginal as & increases further, suggesting that a moderate number of retrieved se- quences is sufficient.
Electricity
0.14
Number of Retrieved Seq, (K)
Figure 6: Parameter sensitivity the number of retrieved sequences (k) on seven zero-shot evaluation datasets.
B.4 Impact of Retriever Encoder Choice
To further investigate the impact of retriever encoder choice, we conduct an ablation study using pretrained encoders from two additional TSFMs, namely TTM and MOMENT, alternatives to the
25

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Table 10: Performance comparison of TS-RAGC Chronoooos-bblltt using different Retriever Encoders
(Chronos, TTM, and MOMENT). Both MSE and MAE are reported.
Retriever Encoder
ETThl
ETTh2
ETTm1
ETTm2
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
w/o RAG
0.3616
0.3650
0.2517
0.2992
0.3109
0.3185
0.1487
0.2236
Chronos
0.3557
0.3624
0.2451
0.2982
0.2906
0.3114
0.1466
0.2231
TTM MOMENT
0.3556
0.3625
0.2465
0.2993
0.2906
0.3121
0.1452
0.2217
0.3553
0.3635
0.2437
0.2981
0.2967
0.3164
0.1465
0.2234
Chronos encoder used in our main experiments. TTM employs an MLP-Mixer-lins like architecture, while MOMENT is based on a Transformer encoder. The Chronos encoder is built on TS architecture. All experiments follow the same retrieval-aus -uggeeete setup for fair comparison.
Table 10 reports the results. We observe that the performance across the three encoders largely comparable all datasets, and encoder consistently outperforms the others. This suggests that the choice of retriever encoder architecture (among existing TSFMs) has limited impact on the overall performance of TS-RAG.
Table 11: Zero-shot forecasting results under different retrieval distance metrics. Both MSE and MAE reported.
w/o RAG
Euclidean
Cosine
DTW
Dataset
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
ETThl
0.3616
0.3650
0.3557
0.3624
0.3558
0.3624
0.3606
0.3647
ETTh2
0.2517
0.2992
0.2451
0.2982
0.2465
0.2982
0.2511
0.3001
ETTml
0.3109
0.3185
0.2906
0.3114
0.2906
0.3111
0.3100
0.3183
ETTm2
0.1487
0.2236
0.1466
0.2231
0.1458
0.2224
0.1489
0.2240
Exchange
0.0673
0.1780
0.0627
0.1718
0.0624
0.1716
0.0665
0.1776
B.5 Effect of Retrieval Distance Metrics
To further investigate the effect of retrieval metrics, we conduct additional ablation studies using cosine similarity and DTW distance. For a fair comparison, cosine similarity is calculated over the embeddings generated by the Chronos encoder, while the DTTW distance is computed directly in the original time-series space.
From the results in Table IT. we observe that Euclidean and cosine distances achieve very similar performance, both clearly outperforming the baseline setting (i.c., without RAG). In contrast, the DTW distance only achieves modest improvement over the baseline, and in some cases even performs slightly worse. This indicates that embedding- -based retrieval methods are more effective and robust for identifying relevant forecasting references than distance measures computed directly in the raw time-series space. Notably, this finding also aligns with prior observations (50) that correlation-bast methods are significantly inferior to embedding basedd methods for retrieving forecasting references.
B.6 Comparison between TS-RAG and RAF
As shown in Table 12, the original RAF implementation (RAFchurene) exhibits significantly inferior performance compared to TS-RAG across all benchmarks. Even when upgrading the backbone of RAF to Chronos- Bolt TRAFFChronssRaall Bolt), PS-RAGGhnonssB.p Bolt still consistently outperforms RAF on both MSE and MAE metrics. These results demonstrate the effectiveness of TS-RAG's overall design, which integrates both an optimized retrieval process and an adaptive augmentation module to enhance zero-shot forecasting performance.
26

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Table 12: Full results for zero-shot forecasting comparison between TS-RAG and RAF.
Method
Metric
ETTh1
ETTh2
ETTml
ETTm2
Weather Electricity
Exchange
Average
MSH
0.4212
0.2661
0.3927
0.1659
0.1726
0.1377
0.0666
0.2318
RAFChrono
MAE
0,3800
0.3137
0.3695
0.2521
0.2048
0.2189
0.1774
0.2738
MSE
0.3660
0.2524
0.3058
0.1477
0.1780
0.1185
0.0632
0.2045
ChCrmosss-bbllt
MAB
0.3710
0.3046
0.3285
0.2281
0.2065
0.2114
0.1725
0.2604
MSH
0.3557
0.2451
0.2906
0.1466
0.1454
0.1120
0.0627
0.1940
TS-R IS-RAAGihonnssbbr
MAE
0.3624
0.2982
0.3114
0.2231
0.1771
0.2002
0.1718
0.2492
(Input)
(GroundTruth)
Top-1 Retrieved seq
Top-2 Retrieved seq
Top-3 Retrieved seq
Figure 7: Retrieval results from the ETThl dataset.
C
Showcases
C.1 Case Studies on Retrieval Effectiveness
Figures Fnd and [ illlsttrate the retrieval performance of TS-RAG on the ETTh1 and ETTh2 datasets. The retrieval results demonstrate that TS-RAG effectively identifies historical patterns with strong structural similarity to the input, particularly in terms of periodicity and trend dynamics. In ETTh1, the retrieved sequences capture complex fluctuations and local variations, aligning well with the seasonal patterns of the input. Meanwhile, in ETTh2, where the time series exhibits smoother periodicity, the retrieved sequences show almost perfect alignment, indicating the presence of highly consistent cyclic behavior. These results suggest that retrieval augmentation enhances forecasting by leveraging time series patterns that closely match the current context, particularly in datasets with strong seasonal dependencies.
C.2 Case Studies on Retrieval-Augmented Forecasting
Figures 9 and 10 showcase the impact of retrieval augmentation on forecasting accuracy in the Weather dataset. Figure highlights situation where the baseline TSFM struggles to capture sudden trend shift, leading to a significant forecasting error. By incorporating retrieved forecasting horizons, TS-RAG successfully adapts to the trend change. Figure [00 demonstrates how retrieval augmentation enhances peak prediction. The standard TSFM underestimates the upcoming peak, whereas TS-RAG, guided by similar retrieved patterns, generates more accurate forecast. These case studies illustrate how aetrenel-augmmented forecasting helps models better adapt to complex temporal patterns, improving robustness in real-world forecasting tasks.
27

---

(Input)
y (GroundTruth)
M
Top-1 Retrieved seq
Top-2 Retrieved seq
Top-3 Retrieved seq
Figure 8: Retrieval results from the ETTh2 dataset.
x (Input) (GroundTruth)
Retrieved
(Predicted)
y (Predicted)
w/o RAG
w/ RAG
Figure 9: Retrieval- Augment forecasting results from the Weather example of improving
trend adaptation.
D Analysis on Time Series Characteristics Influencing TS-RAG Effectiveness
further investigate which characteristics of time series data make TS-RAG more effective. Specifically, we analyze four statistical properties of the benchmark datasets: autocorrelation. noise ratio, volatility, and stationarity, and study how they correlate with TS-RAG's performance improvement over its baseline TSFM.
28

---

(Input)
(GroundTruth))
Retrieved y
(Predicted)
y (Predicted)
w/o RAG
w/ RAG
Figure 10: Retrieval-Augment forecasting results from the Weather dataset-example of improving peak prediction.
Autocorrelation measures the strength of temporal dependencies, i.e., how strongly current observations relate to their past values.
Noise ratio reflects the level of irregular fluctuations in the series, computed as the variance ratio of first differences.
Volatility quantifies the variability of the series relative to its mean, computed as the standard deviation divided by the mean value of each sequence. This ratio reflects local fluctuation intensity.
Stationarity estimates how stable the distribution of time series is time, approximated by the variance of first differences.
We compute these characteristics for all samples in each dataset and report their average values rep- resent dataset-level properties. We then calculate the Pearson correlation between each characteristic and the MSE improvement of TS -AR over the baseline model. Results are summarized in Table I3
Table 13: Quantitative analysis of dataset characteristics and their correlation with TS-RAG perfor-
mance gains.
Dataset
Autocorr.
Noise Ratio
Volatility
Stationarity
MSE Diff
ETThl
0.7799
0.4391
3.1655
0.1752
0.0059
ETTh2
0.6070
0.7217
-0.5386
0.0843
0.0066
ETTml
0.8437
0.2915
-0.9014
0.0536
0.0203
ETTm2
0.6848
0.4480
17.9827
0.0348
0.0021
Correlation 0.70
-0.55
-0.65
-0.19
Our analysis shows that datasets with stronger autocorrelation tend to benefit more from TS-RAG.
whereas higher noise levels and greater volatility correspond to smaller improvements. This suggests that the vetrinval-nggmmenaaiion mechanism is particularly effective when temporal dependencies are strong and the underlying patterns are relatively stable.
29

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E Limitations
Limited Modalities. While Getrieval-Augmeniee Generation (RAG) techniques originally stem from the NLP domain, where retrieval often involves textual knowledge, our current implementation focuses solely on time series data due the lack of available multi-modal datasets. Incorporating rich external information sources such as text or structured metadata could further enhance forecasting performance, particularly in scenarios requiring complex contextual understanding. We leave this as an interesting direction for future work.
Limited Application Scenarios. Our current evaluation focuses on standard public benchmark datasets, which, while diverse, may not fully capture the complexity of real-world forecasting scenarios. Applying TS-RAG to broader application domains, such as finance, healthcare, would further validate its generality and practical value. Exploring these real-world settings remains an important direction for future research.
Broader Impact
This work enhances time series forecasting by leveraging RAG improve time series foundation model performance. The broader impact of this work can be multifaceted. It may enhance decision- making in critical domains such finance, healthcare, and environmental monitoring by providing more accurate and reliable forecasts and could lead to better resource allocation, improved patient care, and more effective responses to climate change. No ethical concerns must be considered. The social impacts are significant, as it has the potential to revolutionize our approach to complex time series data and the integration of emerging AI tools, including foundational models. could change how we analyze and leverage time series data in various fields.
30


Reviews:
[{"review_id": "HNeq5kpUHY", "reviewer": "Reviewer_yyAV", "summary": "This 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.", "questions": "Q1: 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.)", "limitations": "See weakness.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "After reading the authors’ rebuttal and other reviewers’ comments, I insist on my original evaluation towards this paper. The main idea of incorporating RAG into time series is interesting. The overall quality of this paper is good. It is a good paper and I recommend it to be accepted.", "rebuttal": "## **W1&Q1: Please provide more explanations for the results in Figure 2, knowledge bases (right).**\n\nThank you for the suggestion. We provide clarification on the settings of in-domain, distribution shift, and cross-domain retrieval in the “Impact of Retrieval Knowledge Base” section (page 7, lines 283–294), along with the corresponding main results. **In the updated version, we will also include a more detailed analysis in the appendix to further support our findings.**\n\nAcross different retrieval knowledge base configurations, each choice leads to improvements over the baseline without RAG. Among them, the in-domain setting yields the highest gain, followed by the multi-domain setting, with the distribution-shift and cross-domain settings performing slightly lower. These results suggest that the relevance between candidate sequences in the knowledge base and the input query plays a crucial role in the TS-RAG framework: the higher the relevance, the greater the improvement can be achieved through retrieval augmentation. This finding provides guidance for building the knowledge base in real applications: a domain-relevant knowledge base is the ideal choice. When the domain-relevant knowledge base is not available, a multi-domain knowledge base containing diverse time series patterns can still be highly beneficial. \n\n## **W2: Some implementation details are provided in the appendices. It would be better to provide selected key information in the main text to improve clarity.**\n\nThank you for your suggestion, we will include the essential implement details in the experimental setup section in the final version. \n\n## **Q2: Whether incorporating external modalities (e.g., text or structured metadata) could further enhance the forecasting performance.**\n\nThank you for raising this insightful point. We agree that incorporating external modalities could further enhance forecasting performance. In this work, we propose TS-RAG, a retrieval-augmented forecasting framework that has been shown to effectively improve the performance of TSFMs, and it also opens the door to integrating additional modalities in the future. \n\nRecent studies have demonstrated that multimodality, particularly the integration of textual information, can significantly improve time series forecasting [1, 2]. Nonetheless, as noted in [3], the effectiveness of such approaches depends on factors such as model capacity, alignment strategies, and the quality of external data. We believe that a strong and comprehensive forecasting system should leverage both retrieval augmentation and external modalities, though further exploration is required. Particularly, we think some questions are worth to explore: \n\n- **When and how to use external modalities**: External information may not always be beneficial. Developing mechanisms to determine when and how to effectively incorporate them is essential. \n\n- **Multimodal RAG**: Extending RAG to handle heterogeneous modalities raises questions about how to model different modalities within a shared embedding space and how to efficiently leverage retrieved multimodal data to augment forecasting. \n\nWe appreciate your thoughtful suggestion and plan to leave these directions for future work. \n\n[1] Liu, Haoxin, et al. ”Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis.” Advances in Neural Information Processing Systems 37 (2024)\n\n[2] Andrew R. Williams, et al. ”Context is Key: A Benchmark for Forecasting with Essential Textual Information.” Advances in Neural Information Processing Systems 37 (2024)\n\n[3] Zhang, Xiyuan, et al. ” Does Multimodality Lead to Better Time Series Forecasting? ” Arxiv (2025)"}, {"review_id": "GcrKMBDCV6", "reviewer": "Reviewer_6MH9", "summary": "The 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.", "questions": "Q1. 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.", "limitations": "1. Explain whether the retrieval component could amplify historical biases (e.g., over-representing dominant markets) and propose mitigation (balanced retrieval sampling, debiasing losses)", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "My concerns have been thoroughly addressed through the rebuttal, so I raise my score accordingly.", "rebuttal": "## **W1&Q1: Report results for at least one stronger retrieval scheme: cosine similarity, DTW-aware embeddings, or a learned metric layer.**\n\nThanks for your insightful comment. To further investigate the effect of retrieval methods, we conducted additional ablation studies using **cosine similarity and DTW distance**. For a fair comparison, we calculated cosine similarity over the embeddings generated by Chronos encoder, while the DTW distance was computed in the original time series space.\n\nThe results (MSE/MAE) are shown as below,\n\n| Dataset   | w/o RAG        | Euclidean      | Cosine         | DTW            |\n|-----------|----------------|----------------|----------------|----------------|\n| ETTh1     | 0.3616 / 0.3650 | **0.3557** / **0.3624** | 0.3558 / 0.3624 | 0.3606 / 0.3647 |\n| ETTh2     | 0.2517 / 0.2992 | **0.2451** / **0.2982** | 0.2465 / 0.2982 | 0.2511 / 0.3001 |\n| ETTm1     | 0.3109 / 0.3185 | **0.2906** / 0.3114 | 0.2906 / **0.3111** | 0.3100 / 0.3183 |\n| ETTm2     | 0.1487 / 0.2236 | 0.1466 / 0.2231 | **0.1458** / **0.2224** | 0.1489 / 0.2240 |\n| Exchange rate | 0.0673 / 0.1780 | 0.0627 / 0.1718 | **0.0624** / **0.1716** | 0.0665 / 0.1776 |\n\nFrom these results, we observe that **Euclidean and cosine distances achieve very similar performance**, both clearly outperforming the baseline setting, i.e., without (w/o) RAG. In contrast, **DTW distance only achieves modest improvement** over the baseline setting. In some cases, DTW even perform slightly worse than the baseline setting, which suggests that it does not work well to retrieve the sequences that are beneficial to forecasting. Notably, this finding is consistent with the results in [1], which report that correlation-based methods are significantly inferior to the embedding-based methods for retrieving forecasting references. \n\nWe provide two empirical explanations for this finding:\n\n- Retrieval directly in the raw time series space tends to introduce more noise, making it harder to identify sequences that are truly useful for forecasting.\n\n- The retriever encoders we use are pretrained on forecasting tasks, which equips them with the ability to identify sequences with similar future dynamics, since sequences with similar horizons naturally have closer embeddings.\n\nBased on these experiments and the results in Table 10 (in Appendix B.5, page 17) of our paper, we summarize the effect of retrieval methods as follows: \n\n1. The choice between Euclidean and cosine distance has marginal impact on performance. \n\n2. Using different pretrained encoders also produces only a relatively small impact. \n\n3. The embedding-based methods are consistently better than DTW distance in the retrieval-augmentation task.\n\n\n## **W2&Q2: Add at least one proper probabilistic metric comparing TS-RAG to the backbone and RAF.**\n\nThanks for the comment, we followed the settings in our experiment and report the probabilistic metric (WQL) for Chronos-bolt, RAF [2], and ours for comparison: \n\n| Dataset | TS-RAG (Chronos-Bolt) | RAF (Chronos-Bolt) | Chronos-Bolt |\n|---------|------------------------|--------------------|--------------|\n| ETTh    | **0.123**                  | 0.131              | 0.134        |\n| ETTm    | **0.105**                  | 0.111              | 0.116        |\n| Exchange rate | **0.015**                  | 0.016              | 0.017        |\n\nTS-RAG consistently achieves the best probabilistic forecasting performance compared to other strong baselines on various datasets.  \n\n## **W3: In-domain knowledge base requirement at inference time.**\n\nThank you for pointing this out. We agree that domain relevance plays an important role for TS-RAG. To address this, we conducted comprehensive ablation studies on the choice of knowledge base (in section 4.3, page 7, lines 283-294). The results (Figure 2 in page 8, and Table 6 in page 15) show that **TS-RAG achieves consistent MSE improvements across all settings**, including in-domain, multi-domain, distribution-shift, and cross-domain. This demonstrates the robustness of our framework. \n\nIn addition, the ablation study provides practical guidance for real applications. It shows that using a domain-relevant knowledge base leads to the best results, while multi-domain or cross-domain knowledge bases can still deliver meaningful gains when domain-specific data is not available. \n\n## **Q3: Concern about overlap between test split and retrieval index.**\n\nThank you for the comment. To avoid retrieving sequences that overlap with the test queries, **we build the knowledge base strictly from the training split and exclude the validation and test splits. This design ensures that all retrieved windows come from non-overlapping time blocks.**\n\nFor example, in ETTh1 dataset, **the timestamp cutoff for the knowledge base is 2017/6/25 23:00, while the test set starts from 2017/10/2 15:00**. This approach ensures that the retrieved sequences have no overlap with the input queries. We hope this clarification helps address your concern. \n\n## **L1: Whether the retrieval component could amplify historical biases.**\n\nThank you for raising this concern. We agree that retrieval could potentially amplify historical biases if the knowledge base over-represents certain markets or patterns. Nevertheless, as noted on page 6, lines 206–214, in our current design **the attention-based ARM module dynamically leverages multiple retrieved sequences to augment forecasting. This mechanism helps the TS-RAG framework remain robust even when the knowledge base may contain some bias.**\n\nThat said, we acknowledge this as a potential limitation in broader real-world applications, and we will include the following discussion in the limitations section.  \n\nTo mitigate such risks, we see two promising directions: \n\n- **Balanced retrieval sampling**: Applying balanced retrieval sampling to improve the diversity of retrieved sequences \n\n- **Noise injection**: Add controlled noise into the retrieval results to reduce over-reliance on dominant patterns. Notably, [3] has observed benefits of noise for RAG systems. \n\n[1] Liu, Jingwei et al. Retrieval-augmented diffusion models for time series forecasting,Advances in Neural Information Processing Systems 37 (2024).\n\n[2] Kutay Tire et al. Retrieval Augmented Time Series Forecasting, Association for the Advancement of Artificial Intelligence (2025).\n\n[3] Florin Cuconasu et al. The Power of Noise: Redefining Retrieval for RAG Systems, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2024)."}, {"review_id": "Hiu8AKI3ZN", "reviewer": "Reviewer_gudt", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "The authors have thoroughly addressed all reviewer concerns in their rebuttal, providing clear, well-supported responses that directly engage with each point. Their answers are logically structured and backed by empirical evidence (e.g., ablation studies, correlation analyses, and extended experiments with additional architectures), which strengthens confidence in the robustness and generalizability of their work. Notably, they:\n- Clarified limitations (e.g., error accumulation in rolling strategies) while proposing actionable future solutions.\n- Expanded empirical validation (e.g., testing TS-RAG on TTM and TimeMoE, analyzing data characteristics) to substantiate claims.\n- Acknowledged practical challenges (e.g., cross-domain retrieval) and outlined optimization strategies.\n- Demonstrated interpretability through attention weights and retrieval dynamics, addressing transparency concerns.\n\nThe rebuttal also reflects the authors’ receptiveness to feedback, as they committed to revising the paper to better highlight contributions (e.g., refining the experiment section) and incorporating additional visualizations. While the original submission’s clarity could be improved, the rebuttal effectively bridges this gap with precise explanations and new analyses.\n\nI decide to maintain my initial score. The authors’ comprehensive and constructive responses have resolved all raised issues, and their proposed revisions promise to further enhance the paper’s impact.", "rebuttal": "## **W1: Performance depends heavily on the retrieval knowledge base composition.**\n\nThank you for raising this concern. We agree that domain relevance plays an important role. To address this, we conducted comprehensive ablation studies on the choice of knowledge base (Section 4.3, page 7, lines 283–294). The results (Figure 2 in page 8, and Table 6 in page 15) show that **TS-RAG achieves consistently performance improvement across all settings**, including in-domain, multi-domain, distribution-shift, and cross-domain. This demonstrates the robustness of our framework. \n\nMoreover, the ablation study provides practical guidance for real-world applications. It indicates that a domain-relevant knowledge base can yield the ideal results, while multi-domain and cross-domain knowledge bases are still beneficial to improve the forecasting performance when domain-specific data is unavailable. \n\n## **W2: Long-horizon forecasting relies on rolling strategies, risking error accumulation.**\n\nThank you for raising this issue. We agree that error accumulation from rolling strategies is a common challenge for current TSFMs and is beyond the main scope of this paper, which focus on improving TSFMs with RAG (with ARM strategies). To address this, we see two possible solutions: \n\n- **Multi-length prediction heads**: Incorporating multi-length prediction heads into the foundation model architecture could help the model better adapt to varying forecast horizons. \n\n- **Longer horizon projection**: Enabling the model to directly project longer future horizons from the hidden embeddings during training and inference could reduce the number of rolling steps required. \n\nWe hope this discussion could contribute to the better design and development of TSFMs in the future. \n\n## **Q1: Discuss the model's internal decision-making process (e.g., attention weights in ARM) enhances interpretability beyond retrieving similar segments.**\n\nThank you for this insightful question. As noted in Section 4.5 (pages 8–9, lines 327–331), beyond retrieval similarity, **TS-RAG provides interpretability through the transparent attention weights assigned to the retrieved embeddings**. These weights are dynamically generated during training and inference. \n\nFor example, given an input query, we can examine the learned weights of the retrieved embeddings. We consistently observe that a few embeddings receive higher weights while others are assigned lower values. This indicates that some retrieved sequences have a stronger influence on the forecasting process than the others, thereby offering interpretability about how the model makes augmentation decisions. \n\nSuch transparency can be particularly valuable in real-world applications, where understanding which retrieved patterns affect the forecast helps users build trust in the model’s predictions. While figures are not allowed in the rebuttals, we will add an illustration of attention-based interpretability to the updated manuscript to better explain this.\n\n## **Q2: 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\nThank you for your thoughtful question. We agree that the characteristics of time series data, such as stationarity, noise level, and the recurrence of patterns, can influence how much TS-RAG improves over baseline TSFMs.\n\nTo evaluate this point, we analyzed key characteristics of the seven datasets in the paper. Specially, we measured (1) autocorrelation, which captures the strength of temporal dependencies, (2) noise ratio, reflecting the level of irregular fluctuations, (3) volatility, quantifying the variability relative to the mean, and (4) a stationarity proxy, estimating how stable the series’ distribution is over time. We computed these characteristics using standard statistical measures: lag-1 autocorrelation, variance ratios of first differences (noise ratio), normalized standard deviation (volatility), and variance of first differences as a proxy for stationarity.\n\n| Dataset | Autocorrelation | Noise Ratio | Volatility | Stationarity | MSE Diff |\n|---------|----------------|-------------|------------|--------------|----------|\n| ETTh1   | 0.7799         | 0.4391      | 3.1655     | 0.1752       | 0.0059   |\n| ETTh2   | 0.6070         | 0.7217      | -0.5386    | 0.0843       | 0.0066   |\n| ETTm1   | 0.8437         | 0.2915      | -0.9014    | 0.0536       | 0.0203   |\n| ETTm2   | 0.6848         | 0.4480      | 17.9827    | 0.0348       | 0.0021   |\n| Corr.   | 0.70           | -0.55       | -0.65      | -0.19        | --       |\n\nAfter we got all the characteristics, we calculate the pearson correlation between the performance improvement and these characteristics. **Our observations suggest that datasets with stronger autocorrelation tend to benefit more from TS-RAG, while higher noise and volatility are associated with smaller improvements**. This supports our hypothesis that retrieval-augmentation mechanism is most effective when temporal dependencies are strong.\n\n## **Q3: Test TS-RAG with other architectures.**\n\nThank you for your suggestion. We have evaluated the TS-RAG framework on two TSFMs, Chronos-bolt and MOMENT (see page 14, lines 584–591, and Table 4), and observed consistent improvements over their respective baselines. While these results demonstrate the effectiveness of our approach, we agree that extending the evaluation to more TSFM architectures would further strengthen the generalizability claim. To this end, **we additionally evaluated TS-RAG on two strong TSFM backbones: TTM and TimeMoE**. We observed consistent performance (MSE) improvements on both models, indicating that TS-RAG effectively enhances diverse TSFMs. \n\nNote that we select these two additional backbones because of their superior performance in our experiments. Architectures like Lag-Llama and TimeGPT represent earlier exploration of TSFMs, and TimeGPT is not publicly available due to its commercial use. Thus, we believe the evaluation on these newer and stronger backbones provides a more convincing demonstration of TS-RAG's effectiveness.\n\n| Dataset | TTM w/o RAG | TTM w/ RAG | TimeMoE w/o RAG | TimeMoE w/ RAG |\n|---------|-------------|------------|-----------------|----------------|\n| ETTh1   | 0.3619      | **0.3593**     | 0.3623          | **0.3588**        |\n| ETTh2   | 0.2531      | **0.2442**     | 0.2521          | **0.2437**         |\n| ETTm1   | 0.3152      | **0.3077**     | 0.3213          | **0.3045**         |\n| ETTm2   | 0.1511      | **0.1492**     | 0.1565          | **0.1533**        |\n\nEventually, our experiments cover a diverse set of TSFM architectures. Specifically, Chronos-bolt is a T5 encoder–decoder, MOMENT is a transformer encoder, TTM is an MLP-based architecture, and TimeMoE is a sparse mixture-of-experts transformer. **This diversity demonstrates that TS-RAG is not tied to a specific model type but can consistently enhance a wide range of backbone architectures.**. \n\n## **Q4: Discuss strategies to optimize TS-RAG's performance when only cross-domain or limited knowledge bases are available.**\n\nThank you for raising this point. We agree that the real-world deployment of TS-RAG is truly important. \n\nWhile our experiments (Figure 2 on page 8, Table 6 on page 15) confirm that in-domain retrieval achieves the best performance, **TS-RAG remains competitive under other retrieval settings (multi-domain, distribution shift and cross-domain)**, especially multi-domain retrieval.  \n\nTo further optimize TS-RAG in real-world scenarios without in-domain data, several strategies are promising: \n\n- From the knowledge base side, **a prepared multi-domain knowledge which contains diverse temporal patterns** would be helpful in different scenarios. If it’s also not available, **synthetic sequences with similar statistical properties** (e.g., trend,  periodicity) could be generated and indexed into the knowledge base. \n\n- From the retrieval side, although our current retrieval encoder is frozen during pretraining, **the deployed versions could allow finetuning on target data distribution to better align cross-domain embeddings**. \n\n## **Q5:  Discussion about the potential of Dynamic k-selection mechanism.**\n\nThank you for this insightful observation. While TS-RAG currently uses a fixed k during inference, we find that the optimal choice is quite stable across diverse datasets. Our analysis shows that selecting k within the range of 8–12 is sufficient to achieve at least 98\\% of the optimal performance across all datasets** (see Figure 5, page 16). This indicates that k is a relatively robust hyperparameter for TS-RAG. Nevertheless, we agree that a dynamic selection mechanism is a promising direction. \n\n- One simple approach would be to **set a similarity threshold and retrieve only sequences above that threshold**, thereby avoiding the inclusion of weakly relevant sequences that might dilute the forecast.  \n\n- Another possibility is to **use reinforcement learning to learn k-selection policies in an end-to-end manner**. Here, k-selection can be treated as a discrete decision problem: for each input query and its candidate retrieved sequences, a controller predicts the best k from a predefined range, with forecasting accuracy serving as the reward signal. \n\nSuch mechanisms are well aligned with the plug-in nature of TS-RAG and may unlock further improvements in generalization. We appreciate the reviewer for raising this valuable point and view it as an exciting direction for future work."}, {"review_id": "8gkIpYbzJt", "reviewer": "Reviewer_TKBL", "summary": "This 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.", "questions": "1. 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.", "limitations": "1. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The authors have addressed the concerns about diversity, robustness, and generalizability.\nI am pleased to see that these discussions will be integrated into the revised version of this submission, which has raised my overall ratings.", "rebuttal": "## **W1&Q1: More diversity in TSFM architectures (e.g., TimeGPT, Time-MoE) would strengthen generalizability claims.**\n\nThank you for your suggestion. We have evaluated the TS-RAG framework on two TSFMs, Chronos-bolt and MOMENT (see page 14, lines 584–591, and Table 4), and observed consistent improvements over their respective baselines. While these results demonstrate the effectiveness of our approach, we agree that testing on more TSFM architectures would strengthen the generalizability claim. To this end, **we additionally evaluated TS-RAG on two strong TSFM backbones: TTM and TimeMoE**. We observed consistent performance improvements on both models, indicating that TS-RAG effectively enhances diverse TSFMs. \n\nNote that we select these two additional backbones because of their superior performance in our experiments. Architectures like Lag-Llama and TimeGPT represent earlier exploration of TSFMs, and TimeGPT is not publicly available due to its commercial use. Thus, we believe the evaluation on these newer and stronger backbones provides a more convincing demonstration of TS-RAG's effectiveness.\n\n| Dataset | TTM w/o RAG | TTM w/ RAG | TimeMoE w/o RAG | TimeMoE w/ RAG |\n|---------|-------------|------------|-----------------|----------------|\n| ETTh1   | 0.3619     | **0.3593**    | 0.3623         | **0.3588**        |\n| ETTh2   | 0.2531     | **0.2442**    | 0.2521          | **0.2437**         |\n| ETTm1   | 0.3152    | **0.3077**    | 0.3213          | **0.3045**         |\n| ETTm2   | 0.1511    | **0.1492**    | 0.1565          | **0.1533**         |\n\nOur experiments cover a diverse set of TSFM architectures. Specifically, Chronos-bolt is a T5 encoder–decoder, MOMENT is a transformer encoder, TTM is an MLP-based architecture, and TimeMoE is a sparse mixture-of-experts transformer. This diversity demonstrates that TS-RAG is not tied to a specific model type but can consistently enhance a wide range of backbone architectures. \n\n## **W2&L1: The assumption that sampled data is representative in all domains may not hold.**\n\nThank you for raising this concern. **We would like to emphasize that the random sampling from the Chronos dataset, after the TSMixup process, strongly ensures that the sampled data covers diverse temporal patterns.** More details are as follows:\n\n1. The Chronos dataset spans a wide range of application domains, including energy, transport, healthcare, retail, web, weather, and finance, with sampling frequencies ranging from 5 minutes to yearly. It also includes synthetic data, further enriching the diversity of temporal patterns.\n\n2. In addition, a TSMixup process is applied before the final sampling. Base time series from different training datasets are randomly selected and combined through convex interpolation, which further increases diversity. In our work, we then randomly sampled from the Chronos dataset to construct the training and retrieval knowledge bases, ensuring broad coverage and generality. \n\nThe strong and consistent performance of the multi-domain retrieval setting (Figure 2, page 8; Table 6, page 15) provides empirical evidence supporting the representativeness and robustness of our chosen knowledge base.\n\nFurthermore, our work provides a system-level evaluation of the retrieval-augmentation mechanism for TSFMs in zero-shot forecasting. We acknowledge that our knowledge base may not fully cover all domains, such as rare events. In practice, however, end users can construct knowledge bases tailored to their specific needs.\n\n## **W3&Q2: Justification of ARM design.**\n\nThank you for this comment. We note that the ARM module consistently outperforms the simpler fusion strategy across all evaluated datasets (Table 7 on page 16), which highlights its robustness. In particular, on ETTm1, Weather, and Exchange rate, ARM achieves up to 5.4\\% larger gains compared with gating. \n\nWhile the absolute improvements may appear moderate in some datasets, we believe they are meaningful given the consistent improvement and the challenging zero-shot forecasting setting. Moreover, the consistent gains from a simpler fusion strategy confirm the strength and robustness of our retrieval results, and the additional improvements from ARM further validate the value of its design. \n\nThat said, we agree that the augmentation module could be further enhanced in future work, for example by exploring more adaptive fusion strategies or incorporating cross-modal signals to make the retrieval augmentation even more effective.\n\n## **L2: Large-scale time series retrieval data is unavailable in privacy-sensitive applications.**\n\nThank you for raising this concern. We agree that privacy-sensitive applications, such as patient monitoring, often pose challenges due to limited access to large-scale in-domain time series data. However, while our experiments (Figure 2 on page 8, Table 6 on page 15) confirm that in-domain retrieval achieves the best performance, **TS-RAG remains robust under other retrieval settings, especially on multi-domain retrieval, the other options for knowledge base are still better than without (w/o) RAG**.\n\nFurthermore, for such privacy-sensitive applications, some strategies can be adapted to enhance the TS-RAG system.\n\n- From the retrieval knowledge base side, synthetic sequences with similar statistical properties (e.g., trend, periodicity) could be generated and indexed into the knowledge base.\n\n- From the retrieval side, although our current retrieval encoder is frozen during pretraining, the deployed versions could allow finetuning over the target private data distribution to better align cross-domain embeddings.\n\nWe hope this helps address your concern and will include this discussion in the limitations and future directions.\n\n## **L3: Validate retrieved sequences come from trustworthy sources.**\n\nThank you for this point. We agree that TS-RAG currently does not explicitly verify the validity or trustworthiness of the retrieved sequences as this is not the focus of this work. However, this can be addressed with certain modifications. **Since the retrieval database is constructed either by the model creator or by end users, the source, domain and time period of each time series are known.** This information can be presented to the end user as a form of “citation”, providing transparency and a degree of verification.\n\nFurthermore, to enhance the validity of retrieved sequences, possible directions include:\n\n- Applying filtering methods such as anomaly detection or statistical quality checks. \n\n- Introducing confidence scores or trust metrics for retrieved data \n\nWe will discuss this as future directions."}]

Author rebuttal:

Meta-review: This paper introduces TS-RAG, a retrieval-augmented framework for time series forecasting that enhances the generalization and interpretability of Time Series Foundation Models. The key contribution lies in integrating retrieved historical patterns with a pretrained backbone via an Adaptive Retrieval Mixer, which improves zero-shot forecasting without fine-tuning. Before the rebuttal, the reviewers raised some concerns, such as reliance on knowledge base quality, limited diversity in backbone models, and relatively modest gains of ARM over simpler strategies. During the rebuttal, the authors have satisfactorily addressed these concerns and all the reviewers are positive with this paper. Therefore, I would like to recommend accepting this paper.
Decision: Accept (poster)