Title: Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision
Venue: NeurIPS 2025
Accepted: no
Keywords: Time-series  classification

Reasoning LLMs

Time-series Foundation Models
Abstract: The reasoning capabilities of large language models (LLMs) have significantly advanced their performance by enabling nuanced understanding of diverse tasks. With growing interest in applying LLMs to the time series domain, this has proven nontrivial, as evidenced by the limited efficacy of straightforwardly adapting text-domain reasoning strategies. Although recent work has shown promise in time series forecasting tasks, leveraging reasoning LLMs for time series classification (TSC) tasks remains under-explored, despite their prevalence and significance in many real-world applications. In this paper, we introduce ReasonTSC, a novel framework designed to effectively leverage reasoning LLMs for time series classification through a multi-turn reasoning and fused decision-making strategy tailored to TSC. Rather than relying solely on LLMs' built-in reasoning, ReasonTSC first steering the model to think about the essential characteristics of time series data. Next, it integrates predictions and confidence scores from plug-in classifiers, e.g., domain-specific time series models, as in-context examples. Finally, ReasonTSC guides the LLM through a structured reasoning process: it evaluates the initial assessment, backtracks to consider alternative hypotheses, and compares their merits before arriving at a final classification. Extensive experiments and systematic ablation studies demonstrate that ReasonTSC consistently outperforms both standalone reasoning LLM and plug-in models, and even capable of identifying and correcting errors from plug-in models’ false predictions.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a three-stage reasoning pipeline for large language models (LLMs) to address classification tasks involving time series data. The proposed approach, ReasonTSC, consistently outperforms existing time series reasoning baselines. Notably, it demonstrates the ability to correct erroneous predictions from these baselines by utilizing their logit outputs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper introduces not just the idea of incorporating additional data into LLMs (i.e., logit outputs from time series foundation model (TSFM) baselines), but extends this into a structured reasoning framework composed of three distinct stages.\n\n- The proposed approach consistently outperforms prior time series reasoning baselines and shows the ability to correct their incorrect predictions using logit information.\n\n- A large and diverse subset of datasets from the UCR archive is used for time series classification, enhancing the robustness of the results.\n\n- Figure 7 effectively illustrates the rationale behind ReasonTSC overriding baseline model predictions, making the model's decision process more interpretable.\n\nWeaknesses:\n\nI have a few concerns regarding the experiments. If the authors can address these points I would be happy to raise my score. \n\n1. Experiments should be repeated with multiple random seeds. Then statistical significance of performance gains can be assessed over trials—particularly comparing ReasonTSC with baseline models like MOMENT. This would help clarify whether leveraging LLMs on top of TSFM baselines consistently offers added value.\n\n2. Line 170-174 raises concerns for data leakage between training and test sets. The authors mention that, “ 3-shot time series samples are randomly selected from the testing set and feed to the trained classifier to obtain its classification logins and decision confidence.” The logits, confidence, the ground truth labels are fused as auxiliary information for the LLM. Using samples from the test set is problematic and implies data leakage. It also implies an unfair comparison with the MOMENT baseline if it did not also see the same 3 samples during training (these samples should just be included as part of the training set not considered test set).\n\n3. Section 3.3 raises concerns about evaluation methodology. It appears that tables may report test set performance based on the best outcome from several experiments, with few-shot sample counts varying. If so, this should be clearly stated earlier in the paper. Additionally, the trend observed with 3-shot samples (a performance dip) warrants further analysis—does this trend persist with more samples?\n\nSuggestions: \n\n1. Recommend clarifying in the Table 1 caption that bold entries indicate the best classification performance.\n\n2. Recommend also reporting the percentage improvement over the reference model (MOMENT) in relevant tables and figures.\n\n3. The paper states: “This reveals that with a step-by-step integrative reasoning strategy, ReasonTSC can comprehensively consider time series patterns and auxiliary plug-in model information to correct its primary decision.” Consider validating this with an ablation study comparing correctly overridden predictions with and without the third reasoning stage.\n\n4. It would be informative to show performance for reasoning stages 2 and 3 individually, in addition to the combined effect of all three.\n\n5. Vanilla chain-of-thought (CoT) prompting is not discussed in depth. Include references, examples, and comparisons with ReasonTSC in the main paper to better contextualize differences.\n\nQUESTIONS:\n1. How were the 15 datasets from the UCR/UEA archive selected? What were the criteria for inclusion/exclusion?\n\n2. The paper states: “ReasonTSC with DeepSeek as the reasoning LLM surpasses the plug-in model MOMENT by over 10% on six datasets.” Is this 10% an average improvement, a maximum per dataset, or another measure?\n\n3. Are the same numbers of few-shot training samples used for each LLM? Or does Table 1 report best-case results for each LLM with varying few-shot settings?\n\n4. Lines 311-312 states: “Completely removing outputs of the plug-in model leads to a significant performance decrease.” What is the magnitude of this decrease? Was statistical significance tested?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

comments: SUMMARY:
This paper presents ReasonTSC, a framework for enhancing time series classification using LLMs. It introduces a multi-turn reasoning for understanding time series patterns and combines LLM outputs with plug-in predictions from time-series foundation models. The proposed method demonstrates strong performance on benchmark datasets. 

While the paper is solid and presents non-trivial technical contributions, I think there is room for improvements, e.g., comparison with existing work and clarifying some experimental details.

STRENGTHS AND WEAKNESSES:
**Strengths**
- The motivation is timely and well-discussed. 
- LLMs are properly used for time series tasks. 
- Empirical results demonstrate the effectiveness of the proposed framework. 
- Experiments are comprehensively conducted. 
- Code and datasets are shared for reproducibility. 

**Weaknesses**
- The proposed framework shares similarities with "TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents" (AAAI 2025). Please discuss the technical or conceptual differences. 
- Please provide what Eq. (2) - (4) imply. 
- Could the authors provide some reasoning outputs from LLMs?
- Could the authors share their thoughts on why DeepSeek is particularly strong (compared to Llama and GPT)?
- Please present dataset statistics, e.g., # of samples.
- What is the input lengths used in the experiments? 
- How does the model perform with different input lengths? Can LLMs handle long input time series?
- In Figure 3, why does the performance drop when using 3-shot examples compared to 2-shot?

QUESTIONS:
Please refer to the weaknesses discussed above.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes ReasonTSC, a novel framework that enhances time series classification (TSC) by leveraging the reasoning capabilities of large language models (LLMs). Unlike prior work that directly applies NLP-style reasoning techniques (e.g., Chain-of-Thought) to time series tasks with limited success, ReasonTSC introduces a multi-turn, structured thinking process tailored to time series characteristics. The model is explicitly prompted to reason over patterns such as trends, seasonality, and anomalies, followed by a backtracking step to reconsider alternative predictions. Additionally, ReasonTSC incorporates predictions and confidence scores from pretrained time series foundation models (TSFMs) into a fused decision-making process, allowing the LLM to weigh both its own analysis and external expert knowledge. Extensive experiments across 15 benchmark datasets and 16 LLMs show that ReasonTSC significantly outperforms baseline prompting methods and even corrects errors made by plug-in models. The framework demonstrates that, with appropriate guidance, LLMs can reason effectively over time series data without requiring visual encoders or retraining.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1.\tThe paper presents a clearly motivated and original approach to improving time series classification with LLMs. The proposed multi-turn, backtracking-style reasoning process is thoughtfully designed to guide LLMs to attend to domain-specific time series characteristics. \n2.\tThe experimental evaluation is comprehensive, featuring detailed ablation studies that examine the effects of different modules.\n3.\tThe paper is well-written and logically organized, with a clear presentation of its motivation, methodological design, and empirical validation.\nWeaknesses\n1.\tThe evaluation lacks comparison to existing TS reasoning papers.\n2.\tWhile the method performs well for TSC, it remains unclear whether the proposed reasoning framework generalizes to other TS tasks (e.g., forecasting, anomaly detection) without substantial redesign.\n3.\tThe framework still relies on hand-crafted, multi-step prompts, which may limit its scalability. Could the authors elaborate on how well the approach generalizes across different tasks or datasets?\n\nQUESTIONS:\n1.\tCould the authors provide both qualitative and quantitative comparisons with existing time-series reasoning methods? \n2.\tWhy is the classification task specifically chosen? This choice may significantly limit the applicability of the proposed method. Could the authors elaborate on how well the approach generalizes across different time-series tasks or datasets?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces ReasonTSC, a novel framework for leveraging Large Language Models (LLMs) in time series classification through tailored multi-turn reasoning and a fused decision-making strategy. The key innovation lies in explicitly guiding LLMs to analyze fundamental time series patterns (trend, amplitude, stationarity, etc.) rather than relying on generic reasoning approaches. The framework consists of three reasoning rounds: (1) TS Pattern Reasoning - analyzing fundamental patterns across categories using few-shot examples, (2) Plug-in Model Fusion - incorporating predictions and confidence scores from time series foundation models (TSFMs) as in-context examples, and (3) Integrative Step-by-step Reasoning - evaluating initial assessments, backtracking to consider alternatives, and making final decisions. Extensive experiments on 15 UCR/UEA datasets with 16 LLMs and 2 TSFMs demonstrate that ReasonTSC achieves an average 90% improvement over vanilla Chain-of-Thought prompting and can even correct incorrect predictions from plug-in models.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n**Quality:**\n- Solid experimental design with comprehensive evaluation across 15 datasets, 16 different LLMs (spanning 8B to 671B parameters), and 2 TSFMs\n- Statistically significant results with detailed ablation studies examining each component's contribution\n- Well-motivated approach addressing a genuine limitation - that straightforward application of NLP reasoning techniques fails for time series\n- Clear evidence that the method can override incorrect plug-in model predictions (65-77% override accuracy)\n\n**Clarity:**\n- Well-structured paper with clear progression from motivation through methodology to experiments\n- Effective use of figures, particularly Figure 1 showing the three-round reasoning architecture\n- Comprehensive appendices providing full prompt templates, implementation details, and additional results\n- Clear problem formulation and notation in Section 2.1\n\n**Significance:**\n- Addresses an important gap in applying LLMs to time series tasks beyond forecasting\n- Demonstrates that LLMs can effectively reason about time series when properly guided, contrary to recent negative findings\n- Practical impact shown through substantial performance improvements (20-600% over vanilla CoT)\n- Framework is model-agnostic and works with various LLMs and TSFMs\n\n**Originality:**\n- Novel approach of explicitly guiding LLMs to analyze fundamental TS patterns rather than relying on generic reasoning\n- Creative fusion strategy incorporating TSFM predictions as structured in-context examples\n- First work to systematically show that tailored reasoning can make LLMs effective for TSC\n- Innovative backtracking mechanism in the final reasoning round\n\n### Weaknesses\n\n**Technical Limitations:**\n- Limited to univariate time series (only first dimension of multivariate datasets used) due to context length constraints\n- No theoretical analysis of why the specific pattern set (trend, amplitude, etc.) is optimal or complete\n- The 2-shot and 3-shot example selection appears random - no discussion of selection strategies\n- Computational cost not thoroughly analyzed beyond mentioning API overhead\n\n**Experimental Gaps:**\n- No comparison with recent multimodal approaches that visualize time series for LLMs\n- Missing evaluation on longer time series where context limitations would be more challenging\n- No analysis of failure modes - when does ReasonTSC fail to improve over plug-in models?\n- Limited diversity in plug-in models (only MOMENT and Chronos tested)\n\n**Methodological Concerns:**\n- The pattern identification in Section 3.4.1 shows GPT-4o-mini identifies similar patterns across all datasets, suggesting potential overgeneralization\n- No discussion of prompt sensitivity - how robust is performance to prompt variations?\n- The backtracking mechanism's effectiveness varies significantly across LLMs (Figure 4) without clear explanation\n- Unclear how the method would scale to datasets with many more classes\n\n**Presentation Issues:**\n- Key limitations relegated to appendix rather than main paper\n- Some important ablation results (e.g., ICL example selection impact) only in appendix\n- The connection between identified patterns and final classification decisions could be more explicit\n- Missing discussion of why certain datasets show minimal improvement (e.g., LBBB, RBBB)\n\nQUESTIONS:\n### 1. **Generalization to Multivariate Time Series**\nHow would ReasonTSC handle truly multivariate time series where inter-variable relationships are crucial? Could you provide:\n- A concrete extension strategy for multivariate cases\n- Preliminary results on at least one multivariate dataset using all dimensions\n- Analysis of how pattern reasoning would adapt to capture cross-variable dependencies\n\n\n### 2. **Pattern Selection and Completeness**\nThe choice of six fundamental patterns appears somewhat arbitrary. Could you:\n- Provide empirical or theoretical justification for this specific pattern set\n- Show ablation results removing individual patterns to assess their importance\n- Discuss whether domain-specific patterns might be beneficial for certain datasets\n\n### 3. **Computational Efficiency and Practical Deployment**\nThe paper mentions API costs but lacks detailed analysis. Please provide:\n- Concrete time and cost comparisons between ReasonTSC and baseline methods\n- Analysis of the trade-off between performance gains and computational overhead\n- Strategies for reducing API calls while maintaining performance (e.g., caching, batching)\n\n### 4. **Robustness and Failure Analysis**\nThe results show high variance across datasets and LLMs. Could you:\n- Identify characteristics of datasets where ReasonTSC struggles (beyond the brief mention of LBBB/RBBB)\n- Provide examples of misclassifications where ReasonTSC fails despite correct plug-in predictions\n- Analyze prompt sensitivity through paraphrasing experiments\n\n\n### 5. **Comparison with Visual Reasoning Approaches**\nRecent work shows promise in visualizing time series for LLM reasoning. Could you:\n- Include comparisons with at least one visualization-based approach\n- Discuss the trade-offs between your pattern-based reasoning and visual reasoning\n- Provide empirical evidence for why explicit pattern guidance is superior\n\n\n### Additional Clarifications:\n- Table 10 shows very different pattern identification across LLMs - how does this relate to their final classification performance?\n- How sensitive is the method to the quality of the plug-in model? What happens with a poorly trained TSFM?\n- Could you elaborate on the \"domain-specific knowledge\" mentioned in prompts - how is this determined?\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}