TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
Authors: Wen Ye
Wei Yang
Defu Cao
Yizhou Zhang
Lumingyuan Tang
Jie Cai
Yan Liu
Venue: Accepted by TMLR ()

Abstract: Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning with domain- specific computational tools and error feedback loop, TS-Reasoner enables domain-informed, constraint-aware analytical workflows that combine symbolic reasoning with precise numerical analysis. We assess the system’s capabilities along two axes: 1) fundamental time series understanding assessed by TimeSeriesExam and 2) complex, multi-step inference, evaluated by a newly proposed dataset designed to test both compositional reasoning and computational precision in time series analysis. Experiments show that our approach outperforms standalone general-purpose LLMs in both basic time series concept understanding as well as the multi-step time series inference task, highlighting the promise of domain-specialized agents for automating real-world time series reasoning and analysis.

Published in Transactions on Machine Learning Research (03/2026)
TS- : Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
Wen Ye*
yewenGusc.edu
Deportment of Computer Science University of Southern California
Wei Yang*
wyyang930@usc..cl
Department Computer Science University of Southern California
Defu Cao
defucao Busc. edu
Department of Computer Science University of Souternn California
Yizhou Zhang
yiahou. sashoo.shangyis@ussc...
Department of Computer Science University of Southern California
Lumingyuan Tang
luminggyu&kusc.ede
Department of Computer Science University of Southern California
Jie Cai
cuijie@usc.com edu
Department of Computer Science University of Smuteern California
Yan Liu
yonliu. cs csw.s.ccccccoooooo edu
Department of Computer Science
University of Southern California
openreview. Reviewed on OpenReview: https: / net/
forum? id=ghy7Vigjja
Abstract
Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studics on time scries reasoning remain limited to cither single-step inference or are constrained to natural language answers. In this work, we intro- duce TS- TS-RReasoeer, domainn-pecialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning with domain-specific computational tools and error feedback loop, TS- RSaaoeso enables domain-informed constraint-awart an- alytical workflows that combine symbolic reasoning with precise numerical analysis. We assess the system's capabilities along two axes: 1) fundamental time series understanding assessed by TimeSeriesExxam and 2) complex, multi-step inference, evaluated by a newly proposed dataset designed to test both compositional reasoning and computational preci- sion time series analysis. Experiments show that our approach outperforms standalone general-purpose LLMs in both basic time series concept understanding well as the multi- step time series inference task, highlighting the promise of domain-specializer agents for automating real-world time series reasoning and analysis.
* Equal contribution. Please http::///gtthh...c/eeeeeew/sss// Reasoner for code and dataset release.

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Published in Transactions on Machine Learning Research (03/2026)
1 Introduction
In-context Examples
Question and Input Data
Available Operators
Ovestion idlve
forecast for the
shether there are
Question: Tell
(a) Time Series Model Operators
paver...
program:
Predefined Tasks
Output Data
Program:
AncssiDetoP
Forecasting
NuitiPreOP
calibthreshaP
Question: Please tell me whether there are anomalies (extrene weather events) in 282 hours.
Anomaly Detection
Causal Relationship
Time Series Models
Other Time-series Tasks
Anonal
Red teoP
thtethnnerddP
(b) Numerical Operators
Input
Off-the-shelf Modules
Time Series
Task Decomposer
Module
Manipulatioo
Extraction
Results
Arguments
Volaiiity Calculation
Output Program
Opämzztion Other Moouues
THRES calibrataThreshhe data-AoRi SCORE)
(c) External Data Retrieval Operators
FinAl RESULT VallsiinaryPPdaatt-lla SconE, Thiks)))d)TTthss)
Parse Ingut
API Response
Connected API
Error Feadback
Program Executor
Intermediate Feadback
(d) LLM Generated Custom Operators
Customiized Modules (Python)
Parse Description
Final Result
LLM
Figure 1: The architecture of TS-Reasoner. The LLM work as task decomposer, which learn from operator definitions and in-context examples decompose lask instances into sequence of operators. Then program executor is responsible for solution plan execution and forms feedback loop with task decomposer.
Time series analysis lies at the heart of many high-impact real-world applications, powering decisions in domains such as finance, healtheare, and energy systems (Cheng et al. 2021 Sharma et al 2021 Zhang
2021 Over the past decades, research in time series modeling has seen significant ad vadncemensss not only in canonical tasks such as forecasting, anomaly detection, and classification (Hamilton 2020
et al.
Lim &
Zohren 2021
Ismail Fawaz et al. 2019 Zamanzadeh Darban al.] (202)), but also in the development of
foundation models (Ansari et al., 2024 Liu et al.) 2024b] Garza & Mergenthaler-Caneee Canseco 2023 Goswami
ct al. 2024 These models enable zero-shot inference and achieve strong performance on benchmarked tasks,
substantially expanding the scope of data-driven time series analysis.
Despite this progress, real-world time series analysis rarely conforms to a fixed input-output output paradigm. Practical inference often requires procodural reasoning: retricving auxiliary covariates, invoking appropriate forecasting or diagnostic tools, validating operational constraints, and iteratively refining intermediate re- sults. For instance, electricity load prediction power grid operations typically involves combining exogenous data, applying forecasting models, checking system-level constraints, and adjusting predictions accordingly (WANG et al. 2016). Such workflows are inherently multi-step and compositional, yet are not captured by existing models or benchmarks.
This gap motivates a new task setting that we term multi-step time series inference, in which solving single query requires dynamically composing multiple analytical steps, numerical tools, and domain constraints. While time series foundation models excel at individual subtasks, they lack mechanisms for instruction fol- lowing, workflow assembly, and constraint-aware reasoning. Large language models (LLMs), on the other hand, exhibit strong reasoning and instructionnffolowinng capabilities (Mondorf Plank 2024 Zeng et al., 2023), but struggle with temporal pattern recognition and numerical computation due to discretization arti- facts (Spthhis& & Kawsar 2024) and their optimization for token prediction rather than numerical estimation Li et al. 2024] Cvejoski et al.) 2022 Selvam 2025 Addressing these limitations through full retraining or architectural redesign is costly and inefficient.
In this work, we advocate domain-oriented specialization of LLMs for time series inference via program- aided execution. We introduce TS-Reasoner, a time series domain-specialized agent that decomposes high- level inference queries into structured workflows composed of well-defined time series operators, ensuring
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Published in Transactions on Machine Learning Research (03/2026)
numerical accuracy and reliable execution. Rather than embedding time series reasoning within the LLM itself, TS-Reasoner leverages in-context learning (Brown et. al. 2020) to learn how to orchestrate domain- specific operators for multi-step inference. Standard execution feedback is used as an enabling mechanism to iteratively refine generated workflows when errors occur, improving robustness without retraining.
Robust evaluation of multi-step time series inference requires benchmarks that go beyond single-step predic- tion accuracy. We first assess Rea-Ress'''''s foundational time series understanding using Exam 2024). We then introduce new benchmark composed of curated tasks that reflect recurring chal- (Cai et al.
lenges in scientific and engineering practice, including constraint-aware forecasting in smart grid operations 2021 WANG et al. 2016 Greif ct al.) 1999), anomaly detection with threshold calibration YYan et al. 2021p, p, and causal discovery guided by domain knowledge (Aoki & Ester 2020 (instantiated through a Granger-style predictive notion of temporal influence (Sfth) 2007]). These tasks serve as representative instances of multi-stop time series inference, capturing the procedural and analytical complexity absent from existing benchmarks.
(Han ct al.
(1) We formalize multi-step time series inference as a task paradigm that captures procedural reasoning, constraint handling, and domain knowledge integration, which are largely missing from existing models and benchmarks. (2) We propose TS- RSaaoes,,,r, a domain-orienter time series agent, that performs multi-step inference by composing structured workflows over time series operators, ensuring numerical reliability through programmatic execution. (3) We construct a realistic benchmark grounded in scientific and engineering domains, and demonstrate that #S-Reesoneeroupprforrss general-purpor LLM agents while revealing their limitations in time series settings.
We summarize contributions as follows:
2 Related Work
Traditional Time Series Tasks and Foundation Models Classical time series analysis encompasses key tasks such as forecasting (Ekambaram et al. 2023). imputation (Tshiro et al. 2021), classification (Zaao ct al. 2017), and anomaly detection (Xu et al. 2021), each serving unique purposes across domains. Tra- ditionally, ly, these lasks were addressed by dedicated models optimized for specific objectives, resulting in a fragmented modeling landscape. Inspired by advances in LLMs, recent work has proposed general-purpose time series foundation models (Woo et al.) [222]] Liu et al., 2024bb. LLMTime (Gruver et al., 2024] encodes time series as strings, while TimeLLM (Jin et al. (022) and TimeMoE (Shi et al. 2025) align time series with language representations. TEMPO (Cao et al. 2023) combines decomposition and prompt design to enable generalization to unseen data, and Chronos (Ansari et al. 2024 leverages scaling and quantization for efficient embedding. While these models handle multiple preset tasks within a unified architecture, they re- main constrained by fixed task definitions and lack support for procedural, compositional inference involving dynamic task composition and constraint reasoning.
LLM Reasoning & LLM-Based Agents Large Language Models (LLMs) exhibit strong reasoning capabilities when aided by in-context learning and structured prompting (Huang & Chang 2022
Qiao al.
2022
Ahn et al.
2024.
Techniques such as Chain-of-Thougee encourage explicit intermediate reasoning.
prompting and its extensions (eei et al.
2022
Yao et al.
2023a b Wang et al. 2022 Qu et al. 2024)
while program grogram-based reasoning frameworks Khot et al. 2022 Creswell & Shanahan 2022 Gao et al. 2023
integrate external modules for precise execution, as exemplified by VisProg (Gupta & Kembhavi]
2023) in
the vision domain. To address domain-specific limitations, recent agent systems incorporate structured tool interfaces and feedback mechanisms (Sun et al. 2023 Cai et al. 2025). Examples include SWE-agent (Yang et al. 2024) for software engineering, Honey (Zhang et al. 2024 for materials science, and CRISPR- 2024) for biological sequence editing. Within the time series domain, recent works explore LLM- based agents for incorporating external context or event information into forecasting (Wang et al,,
GPT Huang et al.
2024b Loe ct al. 2025). These approaches primarily target specific predictive tasks and model architectures. In contrast, TS-Reasoner focuses on multi-step time series inference as a task paradigm, emphasizing a domain-oriented operator toolkit and structured execution interface that supports heterogeneous tasks and constraint-awarc workflows. More broadly, our work aligns with the Agent-as-Data-Analle paradigm (Tang et al. 2025), and can be viewed as a -ppeseci instantiation with dedicated operators and evaluation protocols.
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3 SS-Reasoner
In this section, we formally introduce TS-Reasoner which addresses the limitations of standalone LLMs in handling numerical precision and computational complexity in time series analysis by decomposing complex time series inference tasks into structured operator execution pipelines. The core architecture of TS-Reasoner can be expressed
R' TaskDeccoppoor((z, C, feodback'
(1)
[U: feeddaack' if failed or poor quality
if successful and valid
Excc(R)) RR))
(2)
where denotes the input task described in natural language, C' represents the in-context examples containing operator definitions and demonstrated operator usages in response to sample questions, R² = f2. fn] is the reasoning trace consisting of specialized operators planned by the task decomposer at iteration i, y represents TSS-Ressoner's final output obtained from successful execution of R. Sorure@providlee an overview of TS-Reasoner. The framework follows a modular design philosophy, separating high-level reasoning from computational execution. By delegating computationally intensive or mathematically precise operations to specialized operators.
Task Decomposer The task decomposer is embodied by a LLM and is responsible for transforming complex natural language instructions into structured operator exccution plans. Given the definitions of operators in the toolbox and set of example task-solution pairs (x1, R1), (z2, Re), (I Rn); "S-Reasoner learns to map new tasks to appropriate decomposition sequences imitating human breaking down complex problems into manageable subtasks. The in-context learning paradigm does not require expensive retraining and allows LLM to recognize structural patterns in time series analysis workflows.
Specialized Operators TS-Reasoner relies on a suite of modular operators that wrap existing statistical tools, machine learning and deep learning models, and numerical utilities. We adopt state-of-the-ard or widely used components and expose them through a unified interface. Each operator is given an informa- tive name (e-g., ForecastOP, OnomalDetOP, calibrate ThreshOP) and follows consistent, calling structure, enabling scamless orchestration within the exccution pipclinc. This standardized abstraction allows the task decomposer to compose complex workflows from heterogeneous lools in model-agnostic and reusable manner. The available operators can generally be categorized into four groups, each addressing distinct aspects of time scries analysis. 1) Time Series Model Operators These operators form the analytical core of TS-Reasoner, applying machine learning and deep learning models to time series data. They handle canonical time series analysis tasks such as forecasting, imputation, and anomaly detection using models like ARIMA (Box& & Jenkins 1968), Chronos (Ansari et al., 2024), Lag- (Rasul et al. 2023), and MOMENT the task, the amount of input data, and time series model choice, this family of operators can work in zero-shot, mode or finetuning mode. 2) Numerical Operators These provide standard statistical and mathematical functions used throughout the solution pipeline as well as simple shape manipulation operators to ensure seamless passing of variables. For example, operators within this family are equipped with functionalities like computing averages, performing decomposition, measuring volatility, or evaluating distribution properties. 3) External Data Retrieval Operators These operators fetch relevant contextual data from external sources. The implementation of these operators mainly involves wrapping API calls to available services that supply time series based on queries. 4) LLM-Generated Custom Operators To address the impracticality of predefining all necessary time series tools, we intro- ducc a LLM-generated custom operator. This is single operator that accepts natural language prompt and returns executable code tailored to the specific analytical requirement, enabling flexible and extensible pipeline that adapts to diverse task demands. This operator is used during refinement when task-specific post-processing logic is required beyond predefined operators (c.g., constraint-driven adjustment electric- ity forecasting). The current toolkit covers 57 operators, a complete list of operators within each category is outlined in section C
(Goswami et al.) 2024). Depending
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① Composing Task Instance
Evaluation
Task Instruction
Ground Truth Data
Ground Truth Data
Final Result from Execution
Task Choice
Anomaly labels
Time Series
Question Generator
Selected
Causal graph
Future data
Dataset
Timee Series
Evaluator
(in cav Iiles)
structural & constral
3: Iask Type selertion
Status: No
2. patisfaction knowledge Incorpofate ion
5: Context Paraneterization( (sequence length, future length, target
Metric name: Metric value Message.
variable & etc
Addiig Complexity (randonly sample constraint type and select
5.
Ground ruth construction (retrieve from dataget dynamically
constraint value) (
or create evaluation Function)
Quality Netric (accuracy, FI, MAPE, etc.)
Figure 2: The proposed pipeline for multi-step time series inference task generation and evaluation.
Feedback Mechanism for Self-Refinement Once the task decomposer generates a structured exccution plan == [2, [n] that specifies the sequence and order of operatons, the program executor interprets and executes each step accordingly. If execution error is encountered, feedback loop is initiated: the task decomposer is re-invoked to produce revised end-to-end execution plan using the original query, in-context examples, the previously generated solution, and the error message from the current execution attempt (as shown in Equation This error correction process is allowed to retry up to k times, where k is predefined maximum number of retry attempts.
With the abundant suite of available time series foundation models, using time series model operators often necessitates model selection. In realistic settings, an intelligent agent may benefit from intermediate feedback either from automated evaluators or human/csee /external signals refine its model choices. Conditioned on such support. from the evaluation benchmark, TS-Reasoner leverages feedback prediction quality (e-g.. MAPE) to experiment with different model choices and select the best-performing one. Feedback is obtained via within-instance, temporally valid self-cvaluation, where the last 10% of cach time scries is used as a pseudo-holdout for intermediate feedback, while final evaluation is conducted exclusively on the unseen future horizon. The program executor maintains a buffer memory of previonsly explored solution paths to prevent redundant evaluations. If the task decomposer reverts to a previously attempted solution, the program execution proceeds directly completion. This architecture ensures error handling, and adaptive refinement, enabling TS-Reasonerr agent to dynamically optimize multi-step time series inference.
4 Multi-Step Time Series Inference Dataset
To study multi-step time series inference realistic settings, we introduce a benchmark dalaset centered on a task formulation that goes beyond single-step prediction. The dataset is designed evaluate whether system decompose natural-language instruction into multiple reasoning stages, perform time series analysis, incorporate domain knowledge, and enforce explicit constraints during inference. Unlike traditional time series benchmarks that focus on a single task such as forecasting accuracy, our benchmark comprises diverse task types, including constraint-awars forecasting, diagnostic analysis, and Granger-style causal reasoning. Each instance is framed as a natural-language instruction paired with context-rich time series inputs and well-defined evaluation criteria, enabling systematic assessment of both solution validity and inference quality.
We categorize the tasks into two broad classes: predictive tasks and diagnostic tasks. 1) Predictive tasks focus time series forecasting under realistic operational constraints. These include requirements such limiting the maximum allowable load in grid, enforcing electricity load ramp rate restrictions, bounding variability within an acceptable range (Greif et al. 1999 Oreshikn el al. (2200). Such constraints are common in engineering and energy systems, where violations may result equipment damage, service disruption, or regulatory penalties (Nti et al. (2020). Our dataset simulates scenarios where future values must not only be predicted accurately but also satisfy operational constraints or leverage domain knowledge. To reflect practical challenges, include forecasting scenarios with and without covariates data supplied in
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Rates
Energy Prediction
Aete:
Causal Relationship
Extreme Weather identification
Aesation: have loodaaoeer doto for the post 97 wirutes I need to ranage the loos var cnittry so thet it does not esceed 2172: 75 un over the complate tree periid (1 a the ancimm change in Toot nver the cntire periol). Plesse give ne foresoss for the next 18 minutes For Lood sonor. Think soout what could be relevant covartetes that con help forecsst Loodsoover, Your guil is to rake the eest accurate forecast zs passible, refine presicttion resuls taeed the comstroint previsusly descrised, crray. The historicol dota for power soved in veriable VAL. Prseces:
Assacion: 1 have historical Prosuct Dorel opeenns (A
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287 hours. Please tell whether there
to get the causal relationship between each pair the vorlobles. I koow
arceat (extramm weather evenns) where are aromalies if pessent in tits sequonce. Please return many aryy anoealy
variuble
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return reauls as a 10 nany
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Final resalte [8. (8.38237575 0, 1.38237579 a 3.3220859 ... 8, 0.30812486]
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excluation in resutt: t"snatusts 3, "accurcry" 0,2065, 'preciston's 1.0, 'ressllfs 0.9365, "F score!: 0.9082)
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Figure 3: Examples of End-to-End Tasks on Time Series. These tasks require the model to perform multi- step reasoning on time series data. Each solution consists of a transparent sequence of steps, making the reasoning process significantly more interpretable than end-to-end multimodal models.
the question, and also scale multi-grid settings where large volumes of data must be processed across multiple regions. 2) Diagnostic tasks emphasize pattern recognition, calibration, and structural inference. One subclass involves anomaly detection, where the model must identify abnormal patterns in time series, often using reference samples contextual priors such as known anomaly rates or anomaly-free samples to calibrate thresholds (Liu et al.] 2016). These tasks simulate settings like extreme weather detection, where defining anomaly boundaries is not straightforward and depends on prior data distributions. Another subclass of diagnostic tasks centers on causal discovery. Here, the model is asked to infer Granger causal relationships among variables, given partial domain knowledge such as the expected proportion of causal links (Aoki & Ester 2020). These tasks require the integration of weak supervision with statistical reasoning (Huan al. 2020 and offer rich testbed.
Figure 2 shows the pipeline used to generate proposed pipeline. Each task instance is generated via a modular pipeline grounded in real-world multi-step time series analysis. We first survey existing literature to identify three task definitions that demand multi-step inference: constraint-aware forecasting, extreme weather detection with known prior and causal discovery with domain knowledge. These are annotated with natural language templates reflecting their problem definition and objectives. To instantiate a task, we source time series data from datasets from corresponding domains: ERATH (climate), PSML (Zheng et al., 2021) (electricity), and synthetic generators (causal graphs). Each specific task instance is parameterized by selecting time ranges, target variable, and context-speeific constraints/ task specification with varying evaluation critcria across instances, distinguishing from traditional fixed- protocol benchmarks.
mmnstrints/knowllee /knolledge. The result is a dynamic
Evaluation is conducted through a unified but flexible protocol. Each task instance is paired with a dedicated evaluator configured with accoss to ground truth data, contextual information, and task-specific constraints or domain knowledge. The evaluator assesses the final model output along three dimensions: structural correctness (e.g., output shape and format), constraint satisfaction (e.g., anomaly rates, causal priors, load limits), and inference quality using appropriate metrics such as accuracy, F1 score, MAPE. This framework supports rigorous and interpretable assessment across diverse task types. Such dedicated evaluators also support generating intermediate leedback quality signals like MAPE by comparing predictions to ground truth, simulating expert or human feedback in realistic decision decision-making workflows.

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4.1 Task Instance Templates
In this section, provide outline of templates used for each type of tasks. The exact template for each sub question type may vary from each other to best reflect the available information: with and without covariates data supplied, whether multiple time series is supplied to test larger-scale multi-step inference cases, what known priors are provided (anomaly rate or reference free sample or known ratio of causal links), and which constrains are selected (global electricity load variability limit, maximum alloable load, e.t.c). The current scale of the proposed benchmark consists of around 500 specific task instances. Figure demonstrates sample questions generated from the task instance generation pipeline.
Predictive Tasks In predictive tasks, we primarily focus on load-related issues in the energy sector. Specifically, each lest sample provides the model with a natural language question, relevant time series historical data, and operational constraint requirements. The questions are generated by the following templates:
Question Template (Electricity Load Prediction) have historical (influence variables] data and the corresponding target variable data for the past {historical length3 minutes. [1. 1 need to ensure that the marimumn allowable syslem load does not exceed {load value) MW. 2. I require that the system load is maintained above a minimum of fload value] MW. 3. I must monitor the load ramp rate to ensure it does not exceed (constraint value} MW for each time step. 4. need to manage the load variability so that does not exceed {construint value} MW over the given period.] Think about how finfluence variables} influence {target variable]. Please give me a forecast for the next {future length} minutes for target variable. Your gool is to make the most accurate forecast as possible, refine prediction result based the constraint previously described. foutput requèrement: E.g. please return 1D wuappy array]- {dala variable name specification: E.g. The historical dola is stored in variable VAL.J.
Diagnostic Tasks In diagnostic tasks, we primarily used extreme weather detection scenarios in climate science and synthetic Granger causal discovery. Specifically, each task instance includes natural language description of the question, time series, and domain knowledge description. The extreme weather detection questions are generated by the following template:
Question Template (Extreme Weather Detection) I have 2m temperoture data that spans (sequence length hours. Please tell me whether there anomalies (extreme weather events) and where are anomalies if present in this sequence. {1. I also have some anomaly-free 2m temperature data from the same region. 2. know that fanomaly rulio) percent of the times have anomalies. foutput requûrement)- {data variable name specification..
For Granger cansal analysis, we synthesize a set multivariate time series grounded domain knowledge from climate science, finance, and economics. Specifically, we generate time series data according to prede- fined temporal influence structures, where directed relationships are defined under Granger-style predictive notion of causality (Seth 2007), consistent with common observational time series settings. Each test sam- ple consists of multivariate time series dataset accompanied by natural language instruction asking the model to infer directed temporal dependencies among variables, along with expert-provided statistics on the ion of true relationshipe The reasoning model is required to infer dependencies based on the observed data and instructions, without access to the underlying generative process. The evaluation framework then measures performance by comparing the inferred dependency structure against the synthetic ground truth, enabling controlled and automatic assessment of causal reasoning within multi-step inference workflows, The questions generated by the following template:

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Question Template (Granger Causal Discovery) 1 have historical (variable nomes} data and wont to get the causal relationship between each pair of the variables. 1 know that (ratio % of the variable pairs have relationship. Consider the polential influence of each variable on the others in this voriable list: {variable namesj. foutput requirementj. {data variable name specification)
4.2 Dataset Statistics
Table I summarizes the dataset compiled for the multi-stop time series inference task. The energy data with covariates contained electricity load from 6 major electricity grids in the United States (MISO, ERCOT, CATSO, NYTSO, PJM, SPP) across 66 zones for 3 complete years (2018 -2220) with minute level frequeney 2 The energy data without covariates contain both geolocation and load data for sub-regions within electricity grids at an hourly frequency. The data is retrieved from official websites of ERCOT, MISO, NYISQ The climate data consisted of the ERA5 reanalysis dalaset which is the most popular climate dataset. The extreme weather detection dataset is obtained from running scripts in climatclearn scripts
5 Experiments
In this section, we conduct a series of comparative experiments to evaluate the performance various LLM agents on both fundamental and complex time series understanding tasks. The evaluation includes multiple-choice questions assessing basie time series concepts, as well more advanced inference tasks supported by the proposed datasct. Our bascline mod- els include proprietary LLMs: GPT-4o (Hurst el al. 2024j, DeepSeek model (Liu et al. 2024a), reasoning
Noise Understanding
Ressoner DeepSeek
ChatGPT at
Anomaly Detection
ReALL
100%
model GPT-ol (Jaech et al. 2024). 2024). .Additionlll, we assess the ReAct agent based on GPT-4o (Yao al.] 2022), which synergizes reasoning and acting by gen- erating traces for each in an interleaving manner. TS- Reasoner uses GPT-4o (Hurst et al. 2024] the task decomposer. For all experiments, we perform single run using the same hyperparameters for the most de- terministic LLM output decoding: temperature=0.0 and top 1=1.0 for the most, deterministic oulput. All experiments are ran using a single NVIDIA A40 GPU with 48G memory.
Pattern Recognition
Similarity Analysis
Causality Analysis
Figure 4: Strict Accuracy of Re-Reasoner and gen- eral purpose LLMs on the TimeSeriesExam.
5.1 Basic Time Series Understanding
To evaluate the fundamental capabilities of those mod- els, we utilize the publicly available TimeSeriesExam benchmark (Cai et al. 2024) consisted of multiple choice questions. This benchmark assesses understanding across five key time series concepts: Anomaly Detection, Pattern Recognition, Noise Understanding, Causality Analysis, and Similarity Analysis. We use strict accuracy evaluation metrics where only the final answer within the response of L.M is checked
httpa://www.eroot www.:///rrn////o///ooooo
http:////e...cicc....eeeeeeeeeee datasatasst

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Dataset
Number of CSVs
Avg Total Timestamps
Number of Variables
Climate Data
624
526
2048
Energy Data
22
8760
1-3
Energy Data w/ Covariates
66
872601
11
Causal Data
8
529
3 6
Table 1: Dalaset Statistics the rucstr dataset. The exact number of time series are not calculated because it depends on randomly sampled sequence length when generating task instances.
against the ground truth answer instead of the complete response 7 As illustrated in Figure 4 TS- 2S-Reasoner consistently outperforms all bascline models across all dimensions. Notably, general- LLMs exhibit particularly weak performance in causality analysis, where TS- TSsRResoner achieves an 87% improvement over the best-performing baseline. This significant advantage is largely attributed to TS- R5-Ressonerr 's integration with program--ided tools for statistical analysis, enabling it to determine autocorrelation, lag relationships, and causal dependencies through numerical computations. The results underscore the benefits of task de- composition and external program utilization in enhancing time series understanding rather than plain text reasoning.
5.2 Multi-Step Time Series Inference
Table 2: Performance on Multi-Step Predictive Tasks. TSR denotes TS- TSR w/ PQ denotes TS- RS-Ressoner prompted with paraphrased questions. LLM models are equipped with CodeAct agent to receive execution leedback. The average MAPE values are calculated among all succeeded questions and the corresponding standard deviation is calculated.
Construint Type
Max Load
Miu Load
Load Ramp Rate
Load Variabiliyy
Average
MAPE
(Std)]
MAPE (Std)]
MAPE (Sid))
MAPE
Prediction w/ Covariates
TSR
0.0621 10.10)
1
0.0664 (0.06)
0.0719 (0.20)
0.9664
0.0577
(0.06)
0.9861
0.0620
TSR w/ PQ
0.474
0.9457 (0:06)
1
0.0564 (0.06)
0.9444
(10659 (0.17)
0,9641
0.0577 (0.06)
0.5691
0.0567
0.3685 (0.30)
(0:14)
0.7778
(0.19)
0.5
0.0443 (0.05)
o1
0.1250 (0.19)
0.9
0.0748 (0:07)
0.0961 (0.16)
0.6967
0.0556 (0.06)
0.6017
0.0%64
Despoook
0.144 (0.22)
1
0.1387 (0.16)
0.8333
0.1160 (0:24)
0.0867
0.0835 (0.07)
0.5750
0.1212
ReAct
0.9474
(0.00)
0.0630 (0.07)
Chattime
0.8421
0.1275/0.21)
0.9
0.1158/0.14)
0.5007
0.2522(0.34)
0.7222
0.1725(0.18)
0,7828
0.1870
Chronos
0.8947
0.1502(0.21)
0.85
0.1206;0.22)
0.1414
0,2153/0.311 31)
0,7778
0. 0.1221(0.15)
0.7417
0.1521
ARIMA
0.7
(0.33)
0.1579;0.22)
0.1945 25)
(.2805(0115)
0.0000
AutaGluon
0.7368
0.0654 (0.23)
0.8
0.1353/0.22)
0.6111
0.0625(0.06)
0.3988
0.0825(0.08)
0.6342
0.0%55
Prodiction w/o Civeariatee
TSR
0.0798 (0.11)
0.1366 (0.20)
0.85
0.1191 (0.17)
0.85
0.0707 (0.04)
0.9250
0.1031
w/ PQ
(0.11)
(0:20)
(0.05)
0,9250
4o
0.8
0.1607 [7]
0.0
0.2007 (0:20)
0.5
0.5430 (0.28)
0.75
0.1838 (0.12)
012275
0.2220
01
0.85
0,1518 (0.06)
11.8
0:1499 (0.06)
0.65
0.1453 (0005)
0.5
0,1672 (0.07)
07700
0.1268
Despoeok
0.1632 (0.14)
0.2342
0.1655 (0.07)
011822
0.1838 (0.12)
1100
02044 (0:17)
0.4
0.4202
1.75
028861 (1:22)
0-0820
012577
Chattime
0.96
(.1207(0.17)
0.76
0.1161/0.17)
0.7
0.1142(0.07)
0.8
(0.04)
020000
0.1107
Chronoe
0.9
0.1579/0.15 15)
0.0
0.214250.24)
0.8
0,2169/0.1 16)
0.5
0. 1543/0000)
0.85
0.1858
0.1741/0.21)
0.9
0,1616/0.11)
0.120)(0.04)
0.9025
0.1321
AusoGluon
0.6
0.1062(0020)
0.65
0.13335/0.0)
0.55
0.1552(0.06)
0.6
(.3471(0004)
0.6
0.1580
Prediction across Multiple Grids
TSR
0.9
0.1491 (0.18)
0.1614 (0.18)
0.9
0.1285
(0.13)
0,1752 (0.
0,9500
0.1535
TSR w/ PQ
0.85
0.1468 (0.16)
(0:18)
0.56
(0.12)
0.9
0:1458 (1.19)
4n
0.8
0.2318 (0.30)
0.85
0.3071 (0:35)
0.6
0.3077 (0.36)
0.55
0.5098 (0.44)
0.7000
0.3501
0.80
[.3066 (0.37)
0.85
0.2604 (0.26)
0.96
0.1877 (0.23)
0.7
0.3222 (0.34)
0.5377
01.7708
Desperok
0.9008
ReAct
0,75
0,2229 (0.22)
11.65
0.2110 (0.25)
0.85
0.2591 (0.29)
0.8
0,1790 (0.25)
0,7625
0.2175
Chattime
0.8
0.131%/0.13)
1.85
(.1411(0.14)
0.6
0.1688(00.08)
01.5
0.2039
0.6877
011612
Chronos
0.1334/0.19)
(.1001(00.12)
(.1280(0.07)
0.198)((
ARIMA
0.60
0.3975/0.34)
0.6
0.408700.34)
0.76
0.610000.311
0.95
0.3589(0.25)
0.6875
0.4100
AusoGluon
0.85
0.0857 15)
0.85
0. (..16600.22)
0.75
0.0854/0.06)
Given the novel paradigm of this task, there a lack of suitable basclines models. For general purpose LLM agents, we adopt a code-based approach due to two primary challenges: loss of precision of time series data
It. was agreed by the original authors upon communication on this alternttie metric that it better reflects capability of L.MMs.
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Extreme Weather Detoction w/ Reference Data
Extreme Weather Detection w/ Anomaly Rate
Causal Discovery
5.7
0.76
0.66
1.ccore
0.02
0.5
0.72
0.00
0.70
0.50
0.3
0.2
0.3
64
0.5
0.6
0.8
0.3
0.4
0.5
0.6
6.7
0.9
0.5
0.6
2.4 Surcess Rate
6.7
300
Success Rato
Success Rabo
TS-Reasorer
W/ PQ
01
Deepseek
MATMCD
2-Score
Granger Causality
Figure 5: Performance Multi-Step Diagnostic Tasks. A small jittering noise of 0.01 is added to success rate to insting overlapping points. TSR w/ PQ denotes TS-Reasoner when prompted with paraphrased questions.
in textual and image form and the limitation of context length encountered for tasks involving large amounts of time series data. To address these issues, we use the CodeAct agent implemented in agentscope (Gao et al., 2024 Wang et al. 2024a).. The CodeAct agent is equipped with Jupyter Python interpreter, error feedback mechanism and standard machine learning and data processing librarics (e-g., statsmodels, scikit- learn, Granger causality utilities). Baseline models are prompted to autonomously generate code pipelines to solve the given tasks. Each agent has a maximum of interact ion turns to reach a solution. In addition, we include ChatTime (Wang et al., (025) and MATMCD (Shen et al. (024) as representative multimodal time serics reasoning models and include AutoGluon CiooGuuon(Shhhurr et al.) 2023) as an AutoML style bascline. To provide further reference points, we also evaluate classical statistical baselines, including ARIMA (Box & Pierce 1970) for forecasting, Granger causality (Seth, 2007 for causal analysis, and E-score-based methods Rousseeuw & Hubert 2011] for anomaly detection.
Table 2 presents the performance of TS- RS-Ressoner and baseline models on predictive tasks. Mean Absolute Percentage Error (MAPE) chosen as the primary inference quality metric to ensure scalo-invariant eval- uation. TS-Reasoner outperforms baseline models in success rates while simultaneously achieving superior inference quality as evidenced by lower MAPE values. MAPE values exceeding are considered unreasonable inference and counted towards failures. Among successful task completions, TS-Reasoner demonstrates the most performance gain on prediction tasks without explicitly provided covariates tasks. This improvement is largely altributed to its ability to retrieve relevant covariates deemed necessary by the task decomposer.
Figure 5 shows the performance of TS- IS-RResoner and baseline models for anomaly detection with threshold calibration and causal discovery with domain knowledge tasks. Methods positioned toward the upper-right direction which corresponds to higher success rates and higher inference quality among successful cases are preferred. While some baselines attain higher inference-qualitty scores small subset of successful executions, they exhibit substantially lower success rates. In contrast, TS-Reasoner achieves the strongest overall tradeoff, consistently attaining high success rates together with competitive inference quality. This highlights the effectiveness of "S--Resooner in orchestrating structured, operator-baset workflows for complex multi-step time series inference, rather than relying on isolated model performance.
Error Analysis In this section, we examine the failure modes of TS-Reasoner and baseline models. By systematically categorizing errors into execution failures, constraint violations, and insufficient/ mnsuficieet/ttriiaall /tiiiil predic- tions, can isolate the fundamental weaknesses in current approaches. Execution failures include runtime errors invalid outputs, while constraint violations refer to outputs that ignore task-specific requirements such as anomaly rates or variability limits. Inadequate results are defined as those yiclding poor utility, such as MAPE > 1, forecasting accuracy = 0, or Fl score = 0. Figure 0 presents error distributions across differ- ent models on the electricity load prediction task without covariates. TS- Reasoner achieves a stable success rate under both the original and paraphrased-instrrcttion scttings, indicating robustness to instruction varia- tion. The remaining failures are dominated by constraint violations (6.2-7.5%), with the paraphrased setting exhibiting slightly higher rate, which we attribute to increased ambiguity and diversity in how constraints
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100
Percentage
75
50
25
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-4o
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 6: Error distribution different approaches on electricity prediction task without covariates.
Table Performance Comparison with key operators removed on constrained forecasting task.
Constraint Types
TS-Reasoner
No Custom Operator
No Retrieval Operator
SR
MAPE
SR
MAPE
SR
MAPE +
Max Load
1.00
0.0799
0.85
0.0557
1.00
0.1349
Min Load
1.00
0.1366
0.80
0.1559
1.00
0.1723
Load Ramp Rate
0.85
0.1191
0,15
0.1193
0.95
0.1707
Load Variability
0.85
0.0767
0.80
0.0843
1.00
0.1233
Average
0.925
0.1031
0.65
0.1038
0.9875
0.1503
are expressed rather than execution instability. In contrast, baseline models display more frequent execution failures and trivial predictions, leading substantially lower overall reliability.
General-purpose LLMs struggle primarily with execution errors, indicating difficulties generating valid putational pipelines for time series analysis. The ReAct agent, despite its explicit reasoning approach, achieves only a 58.7% success rate with the highost proportion of exccution errors (33.8%) duc to the com- plete lack of error feedback. The absence of execution errors in TS-Reasoner highlights the effectiveness of its modular architecture with well-tested computational components. This analysis underscores the necessity of domainn-peciaalized agents like TS- Re-Reeon for complex time series tasks real-world applications, as evi- denced by its 19-34% higher success rates compared to bascline approaches. On the other hand, ReAct agent with explicit thinking can signifieantly reduce the constraint violation rate but still suffers from execution failures and generating trivial predictions.
Ablation Study Figure 7 presents an ablation study on the Electricity Prediction with Covariates task, where we progressively remove key components of TS-Reasoner to assess their individual contributions. The full agent. achieves a 92.5% success rate with a MAPE of 0.1031. Removing intermediate feedback slightly roduces the success rate to 91.3%, with inadequate results beginning emerge. When both intermediate and error feedback are removed, the success rate drops further to 90.0%, and execution errors begin to appear. The most significant degradation occurs when special operators removed, reverting to general-purpose LLM executor equipped with CodeAct. In this condition, the success rate falls sharply to 73.8%, and the MAPE more than doubles to 0.2220. These results demonstrate that feedback mechanisms play a critical role in securing a high success rate, while specialized time series operators are essential for maintaining strong inference quality.
We further isolate the effects of key operator categories in Table Removing the LLM-generated custom operator substantially reduces the success rate (from 92.5% 65.0% average), the agent loses the
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Table 4: Causal Knowledge task: generalization to more ambiguous qualitative domain knowledge with a single added in-context example. Stock Price Future Volatility task: generalization to unseen domain and instruction.
Task
Causal Known Prior
Stock Future Volatility
SR
Acc f
SR
MAPE +
TSR
1
0.739 (0.13)
0.98
0.620 (0.23)
4o*
0.68
0.674 (0.16)
0.28
0.837 (0.19)
o1* *
0.7
0.674 (0.14)
0.42
0.841 (0.18)
DS*
0.76
0.682 (0.15)
0.2
0.745 (0.30)
ReAct
0.66
0.659 (0.15)
0.16
0.970 (0.06)
100
Percentage
75
50
MAPE:0.1031
MAPE:0.0991
IPPE:0.1093
MAPE:0.2222
25
0
TS Reasoner No Intermediate Feedback No Feedback
No Special Operators
Success Execution Error
Constraint Violation
Other
Inadequate Result
Figure 7: Ablation Study on Electricity Prediction w/ Covariates task. We first remove intermediate feedback only, then remove both intermediate and error feedback (denoted as No Feedback), and lastly remove the special operators and falling back to the general purpose LLM equipped with CodeAct.
ability to perform task-specific refinement under constraints. In contrast, removing external data retrieval operators preserves a high success rate but leads to a pronounced degradation in inference quality, with MAPE increasing by approximately 50% on average. This divergence highlights a clear functional separation: custom operators are critical for ensuring successful task completion under constraints, whereas retrieval operators play a key role in enriching contextual information and improving prediction accuracy.
6 Analysis
Generalizing to Qualitative Constraints and New Domains We further consider the practical util- ity R5-Reesonner to handle novel scenarios without extensive retraining or specialized adaptation. We evaluate TS-Reasoner's adaptability novel constraint types and previously unseen domains through two generalization tasks, with results presented in Table First, we introduce causal discovery task incor- porating qualitative constraints that require reasoning about directional relationships rather than precise numerical quantity. With just single additional in-context example added to demonstrate the handling of such qualitative constraints, TS-Rcasoner achieves strong performance over basclines, demonstrating of- fective transfer learning capabilities. Secondly, we test adaptation to stock future volatility prediction lask featuring unseen domain and instruction from the in-context samples. Re-Reeson requires no explicit in-context examples for this task; instcad, the task docomposer successfully generalizes by using its knowl- edge of toolkit functionalities autonomously construct valid solution paths. These results demonstrate "'--Reasoner's generalization capabilities ies across both new constraint. and application domains.
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7 Broader Impact and Limitation
Compared to traditional time series models, TS- "S-Reasoner extends inference capabilities by supporting com- positional reasoning and multi-step execution. However, it is intended a decision-support and workflow prototyping framework, rather than fully autonomous time series modeling solution. T'S-Reasoner only represents an initial step toward multi-step time series inference and cannot fully understands temporal signals. Unlike multimodal Toundation models, which encode time series data, TS-Reasoner does not process raw time series data directly. Instead, relies on task decomposition and structured execution. Further rescarch effort still needed to address challenges of semantic space alignment between time scries and natural language. One limitation of TS-Reasoneris that generalizing to new tasks that requires tools beyond current toolbox may require additional toolbox engineering such visualization tools. In addition, the suc- cess of RS--Resoneer relies in part manually annotated in-context examples that demonstrate proper task decomposition and tool usage. A future direction of this work is to adaptively refine the in-context set by learning from execution errors and feedback signals, enabling the model to iteratively improve its reasoning workflows over time.
8 Conclusion
In this study, we introduced TS-Reasoner, a time series comain-specialized time series agent for multi- step inference tasks. TS-Reasoner integrates compositional reasoning with structured exccution of expert tools. Through comprehensive benchmarking on TimeSeriesExam and diverse set of real-world-inspired tasks that demonstrate multi-step complexity in nature, evaluated Tes-Ronsss'''s performance against leading general-purpose LLM-based agents. Our experiments demonstrate that TS-Reasoner consistently outperforms baseline models in both success rate and inference quality. The results reveal limitations of current general-purpose LLMs in multi-step time series analysis lasks, including frequent execution errors and suboptimal outputs. These findings suggest that LLMs alone are insufficient for such tasks, and that a hybrid approach combining LLMs with domain-specific operators is essential. By bridging LLM reasoning with time series analytical workflows, TS-Reas Re-asssoeer emerges as a simple yet effective solution, paving the way for more intelligent time series agents.
9 Acknowledgment
This work supported in part by the Department. of Defense under Cooperative Agreement Number 1991NFF-24-2-011
well as NSF project # 2425919 and #2413417. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Dataset-Specific Forecast
-htaset-Shared Forecast
Hist
Pred
DLinear
GPT4TS
TimeLLM
TGForecaster
Moirai
TimesFM
Chronos
Chat'Time
TS-Reasoner
48
24
0.5204
0.4373
0.4211
0.4411
0.5981
0.4851
0.4813
0.4849
0.4194
T2
24
0.5075
0.4253
0.4031
0.3943
0.5776
0.4258
0.4276
0.4307
0.3619
96
24
0.4965
0.3921
0.4392
0.3653
0.5179
0.4054
0.4336
0.3920
0.3538
120
24
0.4796
0.3713
0.3594
0.3594
0.5245
0.3807
0.3902
0.3480
0.3336
Avg.
MAE
0.5010
0.4065
0.4057
0.3900
0.5545
0.4243
0.4332
0.4139
0.3672
Table 5: Evaluation results on the PTF context-guided time series forecasting task. Lower values indicate belter performance.
Feat
Len
GPT4
GPT3.5
GLM4
LLaMA3
ChatTime
TS-Reasoner
64
0.6532
0.3507
0.7319
0,6799
0.9011
0.9967
Trend
128
0.7015
0.5846
0.7574
0.5855
0.9068
0.9927
256
0.7482
0.5028
0.6377
0.6143
0.8843
0.9687
512
0.63/16
0.5903
0.6697
0.6753
0.8231
0.9343
Volatility
64
0,5585
0.5633
0.6797
0.6373
0.7874
0.8900
128
0.4979
0.3839
0.4770
0.4756
0.6954
0.9803
256
0.4624
0.4894
0.5418
0.5246
0.6228
0.9713
512
0.3169
0.3796
0.4549
0.5261
0.5736
0.6493
64
0.3518
0.3428
0.3366
0.3484
0.6639
0.8627
Season
128
0.3515
0.3952
0.3464
0.3958
0.6517
0.8767
256
0.5283
0.5089
0.3892
0.4120
0.6463
0.8810
512
0.4457
0.4889
0.3892
0.4127
0.6244
0.8890
Outlier
0.7230
0.4325
0.5359
0.7051
0.8773
0.8787
128
0.6327
0.5940
0.5298
0.5694
0.9032
0.9827
256
0.6795
0.4579
0.5019
0.5073
0.8593
0.9913
512
0.6219
0.4996
0.2822
0.4085
0.7478
0.9910
Avg. Ace
0.5567
0.4728
0.5163
0.5299
0.7605
0.9210
Table 6: The evaluation result in the time series question answering task. Higher values mean better performance for all motrics. The best results are highlighted in bold.
A Additional Evaluation
Table G presents results on the TSQA beuchmark, which evaluates question answering accuracy across fea- ture groups (trend, volatility, seasonality, and outliers) and increasing sequence lengths 22112226///51 A notable difference between the two methods is their behavior sequence length increases. ChatTime'ss performance generally degrades with longer sequences across most feature groups, whereas TS--Rasooner maintains stable accuracy as sequence length grows with the exception of Volatility. This suggests that TS- Reasoner is less constrained by sequence length due to its operator-based execution and decomposition strategy, rather than relying on direct sequence encoding within the language model. We further evalu- ate TS-Reasoner on established benchmarks from the ChatTime framework assess performance beyond our proposed multi-step inference tasks. Table 5 reports results on the PTF dataset for context-guided forecasting. TS-Reasoner achieves lower MAE than ChatTime across all evaluated history lengths.
B Reliability of Base Model & Cost
Base Model Reliability To assess the robustness of TS- Reasoner with respect to the choice of base language model, we substitute ChatGPT-40 HHurst et al. 2024) with smaller open-source backbone, LLaMA 3.1 70B (Dubey et al.) 2024), and evaluate performance across the same set of tasks (Table 2). Despite the reduced model capacity, TS-Reasoner maintains comparable task success rates and inference
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quality across all evaluated settings. This result indicates that TS-Reasoner's effectiveness does not rely solely on the raw reasoning capacity of the underlying language model; instead, its structured execution pipeline and modular operator design enable robust performance even with weaker decomposer models. These findings suggest that TS-Reasoner can be deployed reliably across environments with varying LLM resources.
Inference Cost Given this backbone-agnostic behavior, we next analyze the inference cost of Te-TTSRReen TS-Reasoner under a multi-turn execution setting. We estimate cost by explicitly separating new imput tokens, cached imput tokens, and output tokens. On avcrage, cach turn produces 63 output tokens, priced $10 per million tokens. With maximum of interaction turns, this results in an output cost of
63 x 5 x 10/1,000,000 x $0.003.
The initial input prompt contains 2,647 tokens and is billed at the new-token rate of $2.5 per million tokens. In subsequent turns, previously seen inputs are reused and charged at the cached-token rate of $1.25 per million tokens. Accounting for token reuse across turns, the cumulative bached-input cost is
2647 x (5 + 2) 1.25/1,000,000 = $0.49.
Overall, the total inference cost is approximately $0.5 per question. Consistent with the reliability analysis above, reliance on proprictary models is not essential: when using an open-source backbone (LLaMA 3.1 70B). "S-RResonerachieves comparable task success rates (94.83% vs. 94.25%) and similar inference quality, while avoiding proprietary API usage.
Table 7: Performance Comparison between TS- RS-Reesoner (GPT-40) and TS--Resonee (LLaMA 3.1 70B)
across various tasks.
Task Decomposer Task
GPT-4o
LLaMA 70B
Success Rate
MAPE
Std
Success Rate
MAPE
Std
Electricity w/ Covariates
Max Load
0.0799
0.1128
0.0990
0.1055
Min Load
0.1951
0.2169
Load Ramp Rate
0.85
0.1191
0.1663
0.85
0.0928
0.0363
Load Variability
0.85
0.0767
0.0422
0.95
0.1534
0.2028
Electricity w/ Covariates
Max Load
0.0621
0.1037
0.0677
0.1020
Min Load
0.0564
0.0605
0.0615
0.0539
Load Ramp Rate
0.0719
0.1991
0.0813
0.2032
Load Variability
0.9444
0.0577
0.0588
0.8889
0.0692
0.0928
Prediction Across Multiple Grids
Max Load
0.9
0.1491
0.1750
0.85
0.1503
0.1800
Min Load
0.1614
0.1773
0.1614
0.1773
Load Ramp Rate
0.9
0.1285
0.1269
0.9
0.1324
0.1257
Load Variability
0.1752
0.2455
0.9
0.1938
0.2520
Task
Success Rate
F1
Std
Success Rate
F1
Std
Extreme Weather Detection Reference Data
0.78
0.9214
0.1082
0.8
0.9232
0.1074
Extreme Weather Detection
Anomaly Rate
0.7569
0.0556
0.7569
0.0556
Task
Success Rate
Accuracy
Std
Success Rate
Accuracy
Std
Causal Discovery
0.7915
0.1246
0.7909
0.1239
C Operator Description
Time Series Models operators
UniPreOP: This operator is primarily used for univariate time scries forecasting, leveraging advanced time series models accurately predict future sequences. It currently supports Llama, Chronos, TimeGPT, TEMPO, Arima shot. inference.
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Data
Question/Task
Planner
Execution Environment
Evaluation
Output
Create lasl/contexticonstreiit
Create querier
Plan execution
Execution
Error faedback
Update plan (sell-correction)
Execution
Intermediate feedback
Update plan (model selection)
Execution
Final result evaluation
Execution Environment
Data
Question/Task
Planner
Evaluation
Output
Figure 8: Illustration of an example 7S--Reaooeer workflow
Error Type
Inadequate Result
Constraint Violation
Others (Shape Misalignment)
have historical Wind Speed, Relative Hanidity
corresponding wind_poser dato the past minutes. need to sanage the Tood variability ty so that it does exceed 0,8505 NM over the given period, Speed, Relative lusidity Lnfiue wind power. Please give se forecast for the next 65 minutes for wind_power, Your gool. is nake the rost possible, refine prediction result based on the constroint previously described.
historical
spans hours. Please tell ze whether there are ancealtes (extreme weather events) and where ancenlies if present in this numpy arrey catling
tesperature
Husidi ty, Salor Angle dota corresponding lond_power dota for the post 188 mi minutes, require that the load sointained
User Instruction
8.78
indieating anosoly ancealy..
Think about how Dew Point, Relotive Humidi ty, Salar Zenith Angle influ load.paver. Please
forecast for minutes lood_power.
Finel, volue: ... 0.85]
Output
Final, value: 10.0...11..0,0.,...
Final_ value:
0.83612264 ....
Firol. vol volue..hsee (67.)
Evaluation Result
('stabus" :0, 'nessape": "unrcosonable prediction, fl score is 0.0%)
('status": o, 'ressage" "Pradicted voriability exceeds the moximuu allowable lirit of 0,03e5 NN.
(tshotus":h :0, "ressoge": 'Prediction Shope Misal Misal igrmint Expecting output of Length 68' 'error':23 :2]
Figure Example Result Errors
MultiPreOP: This is a multivariate time series forecasting operator used to accurately predict the target variable based on multiple exogenous variables. It currently support TimeGPT zero shot inference and linear regression.
OnomalDetOP: This is time series anomaly detection operator used generate anomaly scores based on reconstruction errors. It current support MOMENT foundation model zero shot inference.
trainForecastOP: This is operator that supports training time series forecasting models given input data. It currently supports iTransformer training. Model choice is based on current Time Series Library Leaderboard and more models can be included.
This operator that supports training time series anomaly detection models given input data. It currently supports TimesNet training. Model choice is based on current Time Series Library Leaderboard and more models can be included.
trainADOP:
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Numerical operators
CausalMatrixOP: This operator is used to calculate significance of Granger causality relationship wetw each pair of variables in a time series dataset.
Co: This operator concatenates the two given data horizontally.
Oo This operator converts the input data into binary values based on threshold or percentile value.
calibrateeTnreshOP:: This operator approximates the input data with normal distribution and returns the critical threshold value (3 standard deviations away from the mean) of this distribution.
ApplyOP: This operator applies input function to the corresponding data. There are many more oper- ators in this category that performs tasks as suggested by their names: checkStationaryOO (perform adf test), chockTrendSaatinnaayOPPP (perform kpss test), getChptOP (perform bayesian changopoint detection), get.TrenOPP, compareDisOPP (perform ks test), deteclSpikesSOP, gelCCyclePatternOP, getAmplitudeOn gel- PeriodOP, testWhiteNooieeOP (perform box test), getNoiseCommOOP getAutoCorrOP, getMaxCorrLagOP decomposeOP, getSlidingVarOOP, get getCyclePaateenOO detect Pippeed,,, PedtectSpeedUUDDowndee detectCut- offOP, get TrendCooOOPP VolDetOP
Data retrieval operators
getEny This operator can retrieve specified weather or air quality variables if available from open- meteo API. The input must specify time range, geolocation, as well as resolution (daily or hourly).
getFllcctricitDDaaO... This operator retrieves electricity data given zone code from the official EIA website. The imput must specify grid zone name, time range, and variables to retrieve.
LLM generated custom operators
RefGenOP: This operator is primarily used to generate a corresponding python function based on the requirement described in the prompt.
Error Examples and Additional Error Analysis
This section illustrates an example execution workflow TS- S5-Reasoneer (figure S and representative failure cases, including execution errors, shape mismatches, constraint. violations, and inadequate predictions (fig-
ures and 14p. Examples are drawn from both TS-Reasoner and bascline models. Figures 15
10
11
12
and 13 show the distribution of error lypes across various task settings. Execution errors in TS- Reasoner are minimal, reflecting the effectiveness of operator-based abstraction. However, introducing paraphrased questions occasionally triggers exccution failures, suggesting scnsitivity to linguistic variation. In extreme weather detection task (figure [1], inadequate result is the most dominant failure patterns, demonstrating an inability of baseline models to fully leverage anomaly free reference samples to calibrate anomaly threshold often producing a trivial all-zero labels.
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100
Percentage
50
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-4o
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 10: Error distribution of different approaches on electricity prediction task across multiple grids.
100
Percentage
75
50
25
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-4o
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 11: Error distribution of different approaches on extreme weather detection with anomaly free refer- ence data.
100
Percentage
75
50
25
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-4o
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 12: Error distribution different approaches on extreme weather detection task with known anomaly rates.
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100
Percentage
75
50
25
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-4o
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 13: Error distribution of different approaches causal discovery tasks.
Error Type
Execution Error
Execution Error
have historical Advertising Spend (A), Sales (B), Economic Factors (O), Customer Sentiment (0) data and want get causal of the variables. I know that 41, 66666666666676 of the variable pairs have relationship. Consider the potential influence of each variable the others in this variable list: ['Advertising Spend (A)', 'Sales (B) 'Econonic Factors (O), 'Custoner Sentinent (D)'). Please provide 2d numpy natrix with binary values to indicate whether each pair of vartables has relationship.
load_ pow_r data for ni minutes. I require that the system lood is maintained above a minimum of 1490. 6028757896545 MN. Please give forecast for the next 17 minutes for load_power.
between
pair
User Instruction
could relevant covariates that can help forecast load_pawer. Your goal to make the most occurate forecast possible, refine predi ction result based on the constraint previously described, return
10 numpy arrey.
Execution
occurred: 'n_variaaless
An error occurred: con't multiply sequence by type 'float' final value.
not defined
Evaluation Result
f'status' :0, 'message": "evaluator received input NoneType")
('status' :0, 'nessage"s "An error occurred; TIddxx object has no attribute 'ninute"")
Figure 14: Example Execution Errors.
100
Percentage
75
50
25
0
TS Reasoner
TS Reasoner (w/ PQ)
GPT-40
GPT-o1
DeepSeek
ReAct
Success
Constraint Violation
Other
Execution Error
Inadequate Result
Figure 15: Error distribution of different approaches on electricity prediction task with covariates.
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E
Prompt for TS-Reasoner
def UniPre0P (data, future_ 1ength, model): Channel independent zero shot forecasting tool. For time series with
covariates, this tool can be used by simply providing the main time series data.
Input:
data:
np. array ([T,C)) T-history data len, C=variable number, the main time series
future_lengtin int, the number of steps to forecast
model: str, the model to use for prediction. available models: "TEMPO", ARIMA", "Chronos", "Noirai", "TabPF"", "Lagllama"
Output:
up. apray ([N,C]) cl), N=future @efuture\_length C=variable number, the predicted future values
(more definitions operators)
Example:
Question:
I have 2m_ temperatuur data that spans 219 hours. Please tell whether there are anomalies (extrene weather events) and where are anomalies if present in this sequence. Please
numpy array with 1 indicating anomaly and 0 indicating no anomaly. The data is stored in variable VAL and some anomaly-free normal samples stored variable NORM_ VAL.
Program:
KORR_SCORE AnomslleettPPdaaaattOXX...
THRES calibrateThreshhP (dtta
TEST_SCORE LnonalDetOPddata-Waaa
FINAL_RESULL convertBinary0F TEST threshold=THRB3
(a few more examples)
Question:
I have 2m temperature data that spans 202 hours. Please tell whether there anomalies (extreme veather events) and where are anomalies if present in this sequence. Please
numpy array with indicating anomaly and indicating no anomaly. The data is stored in variable VAL and some anomaly-fres normal samples are stored variable NORM_VAL. Follow previous examples and ansver last question in the same format as previous examples within markdown format ""python"" '..
Given the question and the toolbox definition provided above, please give steps operations from the oprations availble listed above that can ansver the question. Return only toolbox operations and enclose your response in "'python markdown. Do not include any other information in your response. For *PreOP modules, you can recieve intermediate evaluation feedback to help you choose the best model. You should revert to previously best performing model if all other models you tried fail to improve the performance (a lower MAPE value indicates better performance). You will be able regenrate your reaponse upon
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recieving feedback. Your goal is to achieve status of in the evaluation result when possible. For ezample, if model gives lower MAPE value compared to model B, you should choose model You should replace (PLACEHOLDER) with the corresponding argumenta you deem appropriate for the operation functions. DO NOT any other irrelevant operations cuatom python code, follow my examples where solutions should be in such format.
Q. No.
Question
Ambiguity (Reasons)
Level
1
Detecting anomalics in 2-meter temperature time scrics dataset that spans 8.72 days (824 hours) and consists of 253 data points, I need to identify extreme weather events and determine their locations within the sequence. This requires labeling a binary numpy array where 1 indicates an anomaly and indicates a normal value. The input data is stored in the variable "VAL'' and the expected anomaly rate is to be stored in the variable 'ANOMALY_RAA RATE'.
Low (Clear input and output, well-defined task, specific variable names)
2
Indicate the presence of extreme weather events within a 2m temperature dataset spanning 260 hours. Using the provided VAL and NORM VAL arrays, provide a one-dimensional numpy ar- ray where each entry corresponds to one hour, lagged with 1 for an identified anomaly and for a typical observation.
Medium (Did not plain what data each variable contains)
Given 2D array temperature data with a duration of 241 hours, I'd like determine and visualize the occurrence of anomalous events and their corresponding positions within the sequence. To achieve this, I'd appreciate it if you could provide output array of the same length as the original data, where each element indicates whether the corre- sponding temperature reading is anomalous (1) or not (0), alongside some sample normal data for reference.
Medium (Did not define what is event
anomalous but it should be referring to extreme weather events, no clear input variables)
4
Given a dataset of system load power over the past 123 minutes, we need to develop a 69-minute forecast that adheres to a maximum al- lowable system load of 694.4796 MW. The goal to leverage relevant historical data and make an accurate load power prediction, consid- ering constraints and return the forecast one-dimensional numpy array.
Medium (Unclear where data stored in, did not define relevant historical data when it refers to historical load data) High (No input vari- able names, unclear output format)
5 Provide 26-minute load power forecast for 17 distinct electric grid zones, constrained by a maximum allowable system load of 0.9872 MW per zone, based on historical data (1030 minutes) with 17 associated load_power values.
What historical wind power data for 19 electric grid zones over 882 minutes can be utilized create a 56-minute wind power forccast while ensuring each zone's system load remains above minimum threshold of approximately 0.01 MW. The goal is achieve the most accurale forecast possible, taking into account the specified constraint.
High (Unclear input variable names, the constraint number is approximated)
7
Given 70 minutes of historical load power data in variable VAL, de- rive a 37-minute forecast for future load_power while ensuring that the predicted maximum system load does not exceed 1151.4102 MW. What relovant covariates should be considored for this forecast, and how can the accuracy of the prediction be refined given this constraint to produce 1D numpy array result?
High (Multiple tasks mixed, formulated as questions rather than task instructions)
Table 8: Sample paraphrasod questions classified based on ambiguity levels.
26


Reviews:
[{"review_id": "vF9KxpLvqS", "reviewer": "Reviewer_qH5M", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces TS-Reasoner, an agentic workflow for time series, with a corresponding toolkit. TS-Reasoner comprises an LLM that decomposes a task into subtasks, which are then implemented in a type of domain-specific language in the form of a toolkit. The LLM learns this DSL through in-context learning. If the produced code fails in some way, the plan is re-generated with the feedback produced by the error. This is extended to \"multi-step\" time series inference tasks, for which a novel dataset is constructed.\n\nIn general, the paradigm of LLMs for automated analysis is timely, and the general idea of building toolkits for agents makes sense. However, I have several concerns with the manuscript.\n\n**Novelty & Framing** Please clarify the novelty of TS-Reasoner -- using ICL for tool-calling, with some feedback loop to fix errors, seems somewhat standard to me. It seems more reasonable to me that the work is centered on providing a toolkit that is simple and effective -- and as a bonus, quite compatible with LLM tooling -- but this is not how the manuscript is currently framed.\n\nNotably, there are several existing and uncited workflows that incorporate domain knowledge, contextualization, and LLMs in time series prediction. For example, [1] summarizes and incorporates news in forecasting objectives. [2] provides additional methods for contextualizing and predicting with LLMs. [3] provides a fairly extensive survey on the \"Agent-As-Data-Analyst\" paradigm, including a section on time series and an NL2Code paradigm.\n\n**Regarding Causality** The tool `CausalMatrixOP` refers to Granger causality explicitly, but Granger causality does not otherwise appear in the manuscript. Causal discovery for time series is quite a bit broader than just Granger causality, with substantively different methods, assumptions, and answers (c.f., [4]). Please clarify what paradigm is being used.\n\n**Regarding Program Complexity** A premise of the paper is that TS-Reasoner is performing some type of multi-step reasoning, but example generated programs are quite simple. For example, the \"causal reasoning\" example in Figure 3 essentially just calls the wrapper `CausalMatrixOP`, with the extreme weather identification example similarly just calling `AnomalDetOP`. From these examples, it feels that writing the DSL code directly could even be easier/faster than writing a prompt + waiting for LLM generation.\n\n**Feedback Mechanism** The text surrounding the feedback mechanism is quite vague, especially regarding model selection. It's stated that \"conditioned on such support from the evaluation benchmark, TS-Reasoner leverages feedback on prediction quality (e.g., MAPE) to experiment with different model choices and select the best-performing one.\" This raises important methodological questions, e.g., on what data is prediction quality evaluated on? If it's on training data, this can suggest overfitting; if it's on test data, this suggests data leakage; if it's on validation data, how is this validation data specified?\n\n**Baselines** The baselines are a bit unclear to me in Section 5.2. In particular, what libraries do the baseline models have access to? How much prompt engineering was done?\n\n**Writing** I find the writing generally clear, but there are many typos and editorial issues (see Requested Changes below). The manuscript requires a careful read to fix these.", "claims_and_evidence_yn": "No", "claims_explanation": "In addition to the above concerns (regarding feedback mechanism specification, and baselines):\n- It's stated that \"TS-Reasoner dominates all baseline models in this aspect, highlighting its superior ability to integrate domain knowledge, and perform structured reasoning over complex time series analysis tasks.\" There are two potential issues here:\n\t- In extreme weather detection w/ reference data, TSR is not the best with respect to F1 score.\n\t- I'm not convinced this illustrates \"a superior ability to integrate domain knowledge,\" rather than access to a superior time series library. Indeed, this seems to be suggested in the ablation study.\n- Are custom operators ever used in results? If so, how? `RefGenOP` appears in Figure 3, but uses an undefined `refine_requirement`.\n\t- In a footnote, it is written that \"We also share a few sample questions here https://anonymous.4open.science/r/sample_dataset/.\" This link contains only a pkl file; this is, in general, an unsafe file format to load from an unknown source. Therefore, I did not check it. Please, upload these in a safer file format.", "audience_yn": "No", "audience_explanation": "I'm skeptical that this submission is of interest to a subset of the TMLR audience, as-written. In my opinion, there is a potentially interesting library developed here, but its connection to an LLM doesn't seem to constitute a novel agentic framework. Also of note for this question are the many typos, and lack of open-source access to the toolkit (please do point me to this if I missed it in the original submission).", "requested_changes": "Critical:\n- Clarify novelty, as discussed above. In particular, please discuss related work such as [1, 2] (and potentially others from [3]) in more depth.\n- Clarify how causality is to be interpreted, as above.\n- Clarify the feedback mechanism for model selection, as above.\n- Amend or provide additional evidence for statements about \"integrating domain knowledge,\" as discussed in the Evidence section.\n\nMinor:\n- Pie charts are used extensively, but are quite poor for data visualization purposes. Please consider using an alternative type of chart, e.g., stacked bar charts.\n- It's stated that \"the upper-right corner [of Figure 5] represents the ideal problem solver that achieves both a high success rate and high inference quality among successful cases.\" But this isn't true because of axis limits. Please revise the statement.\n\nI additionally have many editorial remarks that should be fixed:\n- Please use `\\citep` where appropriate. Currently, many references appear as in-text citations when they should actually be parenthetical (e.g., in Section 1).\n- \"Using expert demonstration of tool usage in-context learning (Brown et al., 2020)\" in Section 2 reads awkwardly, please revise.\n- In Section 2, the heading \"Task Decomposer\" should not be used as part of the sentence.\n- In Section 2, \"volatility, or evaluating distribution properties.3) External Data Retrieval Operators\" is missing a space between \"properties.\" and \"3)\".\n- In the \"Question Template (Causal Discovery)\" box, there are missing brackets around tjhe second `{variable names}`.\n- In Section 4.2, elements of the list of electricity grids should have spaces between them.\n- On page 8, \"TS-Reasoner 's\" should not have a space.\n- Section 5.2, \"novel new paradigm\" is redundant. Use \"novel\" or \"new.\"\n- Section 5.2, \"Python\" should be capitalized.\n- Section 5.2, \"Among successful task completions,TS-Reasoner demonstrates the\" is missing a space.\n- Section 5.2, \"causal discovry\" should be \"causal discovery\".\n- Appendix C, \"Granger\" should be capitalized in \"granger causality.\"\n\nAs there are many editorial remarks, I suspect this list is not exhaustive, and the manuscript should be carefully checked.", "broader_impact_concerns": "I believe a broader impact statement regarding the limitations of the current models and benchmarking would be helpful to some subset of potential readers, in particular, applied ML practitioners.", "additional_comments": "# References\n\n[1] Wang, X., Feng, M., Qiu, J., Gu, J., & Zhao, J. (2024). From news to forecast: Integrating event analysis in LLM-based time series forecasting with reflection. _Advances in Neural Information Processing Systems_, _37_, 58118-58153.\n\n[2] Lee, G., Yu, W., Shin, K., Cheng, W., & Chen, H. (2025, April). Timecap: Learning to contextualize, augment, and predict time series events with large language model agents. In _Proceedings of the AAAI Conference on Artificial Intelligence_ (Vol. 39, No. 17, pp. 18082-18090).\n\n[3] Tang, Z., Wang, W., Zhou, Z., Jiao, Y., Xu, B., Niu, B., ... & Wu, F. (2025). LLM/Agent-As-Data-Analyst: A Survey. _arXiv preprint arXiv:2509.23988_.\n\n[4] Assaad, C. K., Devijver, E., & Gaussier, E. (2022). Survey and evaluation of causal discovery methods for time series. _Journal of Artificial Intelligence Research_, _73_, 767-819.", "extra_scores": {}, "extra_text": {}}, {"review_id": "4Wsdi9Y0zS", "reviewer": "Reviewer_8oWC", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In the paper the authors propose a multi-step paradigm to design a large language model backboned agent for generic time series tasks including forecasting, inference, anomality detection, etc. They create a new benchmark involving time series tasks requiring multi-step inference including constrained forecasting, anomaly detection and causal discovery. They benchmark their proposed agent entitled TS-Responsor along with language model baselines to demonstrate the significance of the introduction of multi-round coding based solutions.", "claims_and_evidence_yn": "No", "claims_explanation": "I'd like to split the submission into the proposed methodology, a new benchmark dataset and related benchmarking. The proposed methodology itself, which involves the construction of a LLM based agent mainly focusing on coding tools, follows standard sophisticated agent building strategies hence is relatively convincing. My major concerns are about the benchmark dataset and the benchmarking of the methodology.\n\nThe benchmark dataset is of a pretty narrow selections of domains, and the setup seems to be quite tailored to the proposed method.  The contribution of such a dataset to the community of multi-modal time series solutions is unclear given the current amount of baselines and benchmarking involved.\n\nRegard the empirical benchmarking itself, I can see potentially improvements of\n(1) including results on existing multi-modal benchmarks,\n(2) introducing more relevant baseline methods\n(3) including more ablation studies regarding other aspects of the proposed TS-Responsor", "audience_yn": "Yes", "audience_explanation": "The research question regarding general purposed time series agents studied in this submission is well-defined and critical. The method proposed in the submission follows common and empirically tested practices for developing LLM based agents. Researchers and pracitioners in related domains are expected to be interested.", "requested_changes": "The requested changes are mostly ablation studies necessary to demonstrate the significance of the submission:\n\n1. Benchmarking TS-Responsor with the relevant tasks from existing benchmarks (see, e.g., [1][2][3]) to establish more comparison w.r.t. existing methods.\n\n2. More baseline methods should be included, such as\n- a small selection of relevant statistical methods on all tasks.\n- time series specific foundation models on forecasting tasks (e.g., Chronos).\n- a few other milti-modal time series solution when applicable (see, e.g., this survey [4]).\n- LLMs with paraphrasing if applicable.\n\n3. If I understand correctly the selected operators are critical for TS-Responsor. It would be better to have much more ablation on this particular point, for example\n- how and how often is each operator used.\n- a detailed ablation on the inclusion / exclusion of key set or sets of operators.\n\n 4. Consider reporting the cost of LLM calls.\n \n[1] Williams, Andrew Robert, et al. \"Context is Key: A Benchmark for Forecasting with Essential Textual Information.\" CoRR (2024).\n[2] Wang, Chengsen, et al. \"Chattime: A unified multimodal time series foundation model bridging numerical and textual data.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 39. No. 12. 2025.\n[3] Liu, Haoxin, et al. \"Time-mmd: Multi-domain multimodal dataset for time series analysis.\" Advances in Neural Information Processing Systems 37 (2024): 77888-77933.\n[4] Jiang, Yushan, et al. \"Multi-modal time series analysis: A tutorial and survey.\" Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 2025.", "broader_impact_concerns": "n/a", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "jYxzqA1AIV", "reviewer": "Reviewer_axWm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Summary:\n\nThis paper presents TS-Reasoner, a domain-oriented time series inference agent that augments large language models with structured operator-based workflows to enable multi-step, constraint-aware time series reasoning. By decomposing natural language queries into executable analytical pipelines and incorporating error feedback for self-refinement, the framework addresses key limitations of general-purpose LLMs in numerical precision and procedural reasoning. The authors further introduce a realistic benchmark covering predictive and diagnostic tasks with domain constraints, and demonstrate that TS-Reasoner consistently outperforms strong LLM-based baselines in both success rate and inference quality. Overall, the work highlights the promise of domain-specialized LLM agents for automating complex, real-world time series analysis.\n\nStrengths:\n\n1. The paper clearly identifies a practical gap in current time series modeling and LLM-based approaches, namely the lack of support for multi-step, constraint-aware inference.\n2. The proposed TS-Reasoner architecture is well-motivated and modular, effectively separating high-level reasoning from numerical execution to ensure robustness and precision.\n3. The introduction of a realistic, domain-grounded benchmark significantly strengthens the empirical evaluation and goes beyond standard single-task settings.\n4. Experimental results are comprehensive and demonstrate consistent improvements over strong general-purpose LLM baselines across diverse time series tasks.\n\nWeaknesses:\n\n1. The proposed approach relies heavily on manually designed operator libraries and in-context examples, which limits the overall scalability and level of automation of the system.\n2. TS-Reasoner does not directly model raw time series data; instead, it processes time series through external tools, which constrains its potential for deep integration with time series foundation models.\n3. The stability and interpretability of the LLM acting as the task decomposer are not systematically analyzed, particularly regarding robustness under complex or ambiguous instructions.\n4. The newly introduced dataset is relatively limited in scale; although the task types are diverse, it still falls short of fully capturing the complexity and noise present in real-world industrial scenarios.\n5. The experimental evaluation primarily relies on a small number of high-performance proprietary or large backbone models, leaving the adaptability of the framework to smaller or resource-constrained models insufficiently explored.\n6. From a research perspective, the work is more oriented toward time series systems and engineering-focused agent design, with relatively limited connection to current core research hotspots in large language models, such as model architectures, training paradigms, or general-purpose reasoning mechanisms.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Yes. The claims are supported by clear and consistent experimental evidence, including evaluations on existing benchmarks and a newly proposed dataset, comparisons with strong baselines, and ablation studies that validate the effectiveness of the proposed design.", "audience_yn": "Yes", "audience_explanation": "Yes. The paper would be of interest to researchers working on time series analysis, LLM-based agents, and domain-specialized reasoning, as it addresses practical limitations of general-purpose LLMs and proposes a concrete framework and benchmark for multi-step, constraint-aware inference.", "requested_changes": "See the Weaknesses.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)