Title: Bridging Time and Linguistics: LLMs as Time Series Analyzer through Symbolization and Segmentation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Time series; Large Language Models; Cross-Modal Alignment
Abstract: Recent studies reveal that Large Language Models (LLMs) exhibit strong sequential reasoning capabilities, allowing them to replace specialized time-series models and serve as foundation models for complex time-series analysis. To activate the capabilities of LLMs for time-series tasks, numerous studies have attempted to bridge the gap between time series and linguistics by aligning textual representations with time-series patterns. However, it is a non-trivial endeavor to losslessly capture the infinite time-domain variability using natural language, leading to suboptimal alignment performance. Beyond representation, contextual differences, where semantics in time series are conveyed by consecutive points, unlike in text by individual tokens, are often overlooked by existing methods. To address these, we propose S$^2$TS-LLM, a simple yet effective framework to repurpose LLMs for universal time series analysis through the following two main paradigms: (i) a spectral symbolization paradigm transforms time series into frequency-domain representations characterized by a fixed number of components and prominent amplitudes, which enables a limited set of symbols to effectively abstract key frequency features; (ii) a contextual segmentation paradigm partitions the sequence into blocks based on temporal patterns and reassigns positional encodings accordingly, thereby mitigating the structural mismatch between time series and natural language. Together, these paradigms bootstrap the LLMs' perception of temporal patterns and structures, effectively bridging time series and linguistics. Extensive experiments show that S$^2$TS-LLM can serve as a powerful time series analyzer, outperforming state-of-the-art methods across time series tasks.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a framework to repurpose large language models for time series analysis by focusing on the context gaps between natural language and time series data at the representation level. It introduces two core components, including a spectral symbolization that converts time series into sparse frequency-domain representations and then map them to ASCII symbols, and contextual segmentation that reassigns positional indices using GRU to capture temporal continuity of patched inputs. Experiments across multiple tasks demonstrate the effectiveness of the proposed method, including short-term and long-term forecasting, few-shot/zero-shot learning, and classification.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The high-level idea of using Fourier analysis in time series tokenization is somewhat  interesting. The idea of using an auto-regressive model to capture patched input for positional encoding is also well-motivated and interesting.  \n\n2. The proposed method is evaluated across a wide range of tasks, demonstrating certain advantages over the provided baselines.\n\n\nWeaknesses:\n\n1. This paper still lacks comparisons and discussions with several recent and relevant baselines in LLM-based time series analysis, particularly those focused on context modeling, such as TimeCMA [1] and FSCA [2].\n\n2. The ablation study only focuses on long-term time series forecasting, which seems insufficient to demonstrate the effectiveness of the provided component, especially for Q1.  Results of short-term and classification tasks would be needed for a more comprehensive assessment.\n\n3. The authors stated the disadvantages of time-domain tokenization with an example in lines 162-164, which seems not quite clear and precise. It would be better if a frequency-domain example is provided. Moreover, in line 182, the motivation of using ASCII symbols for frequency component tokenization is not clear, more insights will be helpful. \n\n4. In equation 8, it seems that the frequency-domain output *$O^{n}_{T}$* that passes through a sigmoid function only serves as a gating function, in addition to a cosine similarity for weighted fusion. This design is a bit confusing as *$O^{n}_{T}$* is never used as an actual input for fusion.\n\n[1] TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment\n\n[2] Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series\n\nQUESTIONS:\n1) Could the authors provide the evaluation of related LLM-based time series forecasting methods (mentioned above), to validate the claims in terms of outperforming state-of-the-art baselines?\n\n2) Could the author justify the motivation of using ASCII symbols for frequency component tokenization?\n\n3) In the methodology design, why the frequency-domain output in equation 8 only serves a gating function instead of an actual input to be fused?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to enhance the LLM’s perception of structures and patterns of time series data by bridging the modality gap between time series and linguistics. It proposes S2TS-LLM, a framework to repurpose LLM for time series analysis by (1) a spectral symbolization to endow time series with frequency-based textual abstractions (2) a contextual segmentation paradigm to mitigate the structural mismatch between time series and natural language. The proposed method is evaluated on five mainstream analysis tasks following the setting in GPT4TS.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The paper is well structured and the figures are rich and clear to convey related ideas.\n2. The code is provided, and the experiments are extensive, including multiple task,s and the analysis of the core mechanisms of the model is sufficient.\n3. The paper proposes to construct the textual description of each time series sample in the frequency domain, which is a novel perspective compared with previous works, like Time-LLM.\n\n**Weaknesses**\n\n1. The novelty of the paper is limited, as there have been several works focusing on bridging the modality gap between time series and language by aligning textual and temporal semantics, like Time-LLM, TEST, CALF [1], and S2 IP-LLM. Moreover, the author has not addressed the shortcomings of previous work, and the proposed innovations are not compelling.\n\n2. The motivation and effectiveness of the proposed Spectral Symbolization are not convincingly established. (**Lines 43-61**)\n    * The authors claim that existing time-domain descriptions fail to capture complex patterns in time series data and advocate for frequency-domain representations. While frequency-domain analysis can effectively capture periodic and steady-state features, it falls short in addressing \"temporal localization\" and \"non-stationary changes,\" which are crucial for identifying time series patterns. Consequently, while frequency-domain descriptions may serve as valuable supplements, they are not inherently superior to time-domain analyses.\n    * I am skeptical about the approach's ability to \"losslessly capture the infinite variability of the time domain\" and to \"describe intricate variations with concise language.\" These objectives seem to be the core challenges that the paper aims to tackle, yet the proposed method does not convincingly demonstrate its effectiveness in achieving them.\n\n3. The observation of assigning sequential indices to individual time points lacks literature support and seems to be imaginary. (**Lines 71-84**) The baselines, including Time-LLM, GPT4TS, and S2 IPLLM, all utilize a patching strategy, with positional encoding implemented at the patch level rather than the time point level. Patches can preserve local temporal context and model continuity in temporal trends within the patch. The author should compare their proposed segment-level positional encoding with the previous patch-level positional encoding, instead of time point-based token-level positional encoding.\n\n[1] Peiyuan, Liu, et al., \"CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning,\" AAAI 2025.\n\nQUESTIONS:\n1. Both previous time-domain descriptions and Spectral Symbolization can provide information about time series patterns from different perspectives. Is Spectral Symbolization superior and can provide more information about time series variations than time-domain descriptions?\n2. Why can ASCII symbols enhance the comprehension of spectral patterns by LLMs? Is there any evidence or literature to support this assertion? (**Lines 181-183**)\n3. Contextual Segmentation groups the patches into blocks to assign grouped indices, which might destroy the temporal order of patches in positional encoding, thus hinder the LLM’s comprehension of structural continuity embedded in the temporal orders, for example, an upward trend patch and a subsequent downward patch construct a peak shape in time series. How to solve this new challenge brought by the proposed Contextual Segmentation? (**Lines 184-200**)\n4. What is the relation between B segments and Q patches? (**Line 193**)\n5. Since this work focuses on LLM for time series analysis, there have currently been many publications using LLM for time series. More experiments about LLM-based methods should be added to explain why the non-LLM baselines should be reduced. The relevant LLM baselines include TEST, CALF, UniTime, TEMPO, and so on.\n6. Why choose GPT-2 as a backbone? For time-LLM, which uses LLaMA as a backbone, the comparison is not fair. Is there any backbone ablation study?\n\nLIMITATIONS:\nyes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a model called S2TS-LLM, which repurposes LLMs for time series analysis. The method consists of two major components: (1) a spectral symbolization module that converts time series into the frequency-domain for textual abstraction; (2) a contextual segmentation module that segments time series and groups positional encodings based on the temporal patterns. The representations are later fed into a pre-trained LLM for downstream applications. Authors performed comprehensive experimental evaluation of the proposed method, showing that S2TS-LLM outperforms state-of-the-art methods across multiple standard time series tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n1. Overall, this paper presents a creative methodology and a valid bunch of innovative contributions\n- The spectral symbolization module emphasizes the frequency domain representation, which aligns with previous works [1, 2]\n- The contextual segmentation module makes valid contribution in terms of emphasizing time series shape structures.\n2. Comprehensive ablation experiments, demonstrating the effectiveness of the proposed components\n- Table 6 is very informative.\n3. Decent amount of experimental evaluation and informative interpretation of the results\n4. Informative and insightful visualizations and analysis.\n\n[1] Liu, Ran, Ellen L. Zippi, Hadi Pouransari, Chris Sandino, Jingping Nie, Hanlin Goh, Erdrin Azemi, and Ali Moin. \"Frequency-aware masked autoencoders for multimodal pretraining on biosignals.\" arXiv preprint arXiv:2309.05927 (2023).\n\n[2] Zhang, Xiang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik. \"Self-supervised contrastive pre-training for time series via time-frequency consistency.\" Advances in neural information processing systems 35 (2022): 3988-4003.\n\nWeakness\n\n1. The work could use more comprehensive experimental evaluation\n- Can the proposed method be used on anomaly detection experiments, and how it would perform when comparing to other baselines?\n- Why only performing classification experiments on 10 UEA datasets? Can the method perform well on all UEA and UCR datasets?\n- When applying the method on different tasks and datasets, did the authors investigate at which condition, or on which dataset, the proposed method can perform significantly better than other methods? In other words, when would the method provide the biggest performance margin?\n2. The work can include more comprehensive literature review\n\nQUESTIONS:\nCan the authors design synthetic experiments or design case studies to understand when will the proposed method have the most significant gains, and when will it fail?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors adapts GPT-2 to time-series tasks through two add-on modules, Spectral Symbolization and Contextual Segmentation. \nSS turn patch into short string of ASCII symbols that stand for the *k* biggest FFT peaks.\nCS use GRU patch embeddings, clustered them and assign unique positional ID. \nThey claim forecasting and classification show improved results compared to prior LLM for TS baselines.\n\nSTRENGTHS AND WEAKNESSES:\n*Strength*\n\n- The amount of empirical validation seems solid with 23 datasets and decent ablations\n- Contextual segmentation is novel and well motivated. Shared positions solve long flat segment problem.\n- Training budget is friendly. Backbone is frozen, only a few norms move.\n\n*Weakness*\n\n- Spectral symbolisation isn’t new. FreqTST (https://openreview.net/pdf?id=N1cjy5iznY) already tokenised FFT peaks. FreEformer (https://arxiv.org/pdf/2501.13989) works fully in frequency space. Though it's understandable that neither is cited because they are not peer reviewed.\n- Evidence for “spectral has *better* information” is one ETTh1 ablation and a pretty heat-map; no magnitude-sensitive or trend-heavy test at all. I will explain why I suspect this idea in limitation.\n- GPT-2 gets per-set LR sweeps while classic models use canned configs.\n\nQUESTIONS:\n1. Does contextual segmentation really beat other long-context tricks (e.g., hierarchical attention, relative positions)? I only see the ablation without it.\n2. Is the simple cosine-similarity gate the best way to fuse the raw-value and spectral streams?\n3. After all, why should casting time-series into a handful of FFT-based ASCII symbols help a frozen GPT-2 transfer its linguistic priors? I don't see even a guess.\n\nLIMITATIONS:\nI feel there are a handful of situations where SS will cause information loss, though they could perform well on some dataset.\n1. Feed it x^2 or log⁡ x; the DC term plus a handful of low harmonics can’t recover the curvature, which can be easily described by text.\n2. “Noisy but meaningful” beats. Heart-rate variability: the clinical signal sits in tiny shifts around 0.1 Hz, but the big energy spike is the main beat at ≈1 Hz.\n3. Highly tortuous trajectories**.** A pedestrian GPS trace is full of zig-zags; FFT shoves all that into a smear of high-freq energy that says nothing about *where* the person is going.\nSS is very much limited to wave-like time series, but in many real world applications time series.\nM4 dataset is a good reference but it contains none of the above situations. The variability is very limited.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}