Time Series Reasoning

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability
Oct 5, 2026Zhuomin Chen, Jingchao Ni, Xu Zheng +4Time Series Reasoning Time Series QA
Florida International University · University of Houston · NEC Laboratories America +1
Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs
Oct 1, 2026Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han +2Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
Layer6 AI
Next-token functional estimation
Sep 17, 2026Milind Nakul, Vidya Muthukumar, Ashwin PananjadyTime Series Reasoning Next-Token Prediction
Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Institute of Technology
Time-Varying Data as Sheaves: an Invitation to Narratives
Sep 8, 2026Wilmer Leal, Benjamin Merlin Bumpus, Jana K. Nickel +3Time Series Reasoning Sheaf Theory
Department of Mechanical and Aerospace Engineering, University of Florida, 1064 Center Drive, Gainesville, 32611-6250, Florida, USA. · Instituto de Matem´atica e Estat´ıstica, Universidade de S˜ao Paulo, Rua do Mat˜ao, 1010, S˜ao Paulo, 05508–090, SP, Brasil. · Fachbereich Mathematik, Universität Hamburg, Bundesstraße 55, Hamburg, 20146, Germany. +2
REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
Aug 10, 2026Xu Zhang, Chang Xu, Hui Sun +5RL for Language Models Explainable Time Series Forecasting
Fudan University Shanghai, China · Microsoft Research Beijing, China · Jilin University Jilin, China
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Aug 3, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series Forecasting Time Series Reasoning
LG AI Research
TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning
Jul 9, 2026Fangxu Yu, Tao Feng, Dehai Min +3Adaptive Model Routing Time Series Reasoning
University of Maryland, College Park · University of Illinois Urbana-Champaign · University of Illinois Chicago +1
From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
Jun 20, 2026Xin Qiu, Junlong Tong, Yao Zhang +3Time Series Reasoning Time Series QA
Institute of Digital Twin, Eastern Institute of Technology, Ningbo · Zhejiang University · Munich Center for Machine Learning, LMU
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
Jun 17, 2026Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen +1Time Series Reasoning Time Series QA
Deakin University
Can LLM Coding Agents Reason About Time Series?
Jun 15, 2026Filip Rechtorík, Ondřej Dušek, Zdeněk KasnerTime Series Reasoning Time Series QA
Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics, Charles University
Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
Jun 13, 2026Sanhorn Chen, Xiaoyang Chen, Boyu Liu +1Irregular Time-Series Modeling LLM Evaluation
University of Illinois Urbana Champaign
Representing Time Series as Structured Programs for LLM Reasoning
Jun 10, 2026Jaeho Kim, Changhun Oh, Seokhyun Lee +2Time Series Reasoning Time Series QA
Korea University · Mila, University of Montreal
Harnessing Generalist Agents for Contextualized Time Series
Jun 3, 2026Zihao Li, Kaifeng Jin, Yuanchen Bei +8Time Series Reasoning LLM Agent Orchestration
University of Illinois Urbana-Champaign
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
Jun 1, 2026Zhensheng Wang, Xiaole Liu, Wenmian Yang +3Table QA Time Series Reasoning
School of Artificial Intelligence, Beijing Normal University, Beijing, PR China · Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, PR China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, PR China +1
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
May 31, 2026Yaxuan Kong, Qingren Yao, Yuqi Nie +7Time Series Reasoning LLM Agent Evaluation
University of Oxford · VulpiVox Intelligence · Eindhoven University of Technology +3
KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
May 28, 2026Kun Feng, Ziwei Shan, Yuchen Fang +6Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
ShanghaiTech University · Ant Group
AION: Next-Generation Tasks and Practical Harness for Time Series
May 24, 2026Tianxiang Zhan, Xiaobao Song, Tong Guan +2Time Series Reasoning Time Series Forecasting
1Griffith University · 2Shenzhen University · 3Zhejiang University
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
May 23, 2026Liying Han, Kang Yang, Oliver Wang +9LLM Evaluation Time Series Reasoning
University of California, Los Angeles · Samsung Research America · Carnegie Mellon University +2
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
May 21, 2026Jialin Chen, Aosong Feng, Harshit Verma +7Time Series Reasoning RL for Language Model Reasoning
Yale University · Arizona State University · University of Texas Rio Grande Valley
Nexus : An Agentic Framework for Time Series Forecasting
May 14, 2026Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6Time Series Reasoning Time Series Forecasting
1Google · 2Pennsylvania State University
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
May 11, 2026Hangchen Liu, Dongyuan Li, Renhe Jiang +3Time Series Reasoning LLM-Based Time Series Forecasting
The University of Tokyo · Southern University of Science and Technology
Reasoning-Aware Training for Time Series Forecasting
May 9, 2026Md Atik Ahamed, Mihir Parmar, Palash Goyal +4Time Series Reasoning Time Series Forecasting
1Google · University of Kentucky
FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
May 5, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +7Time Series Reasoning CoT Reasoning
LG AI Research · Seoul, South Korea
ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response
Apr 23, 2026Stephan Xie, Ben Cohen, Mononito Goswami +6Time Series Reasoning Time Series QA
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA · Datadog AI Research, New York, NY, USA · Amazon Web Services, Seattle, WA, USA
LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
Apr 19, 2026Yueyang Ding, HaoPeng Zhang, Rui Dai +4Vision-Language Models Time Series Reasoning
Amap, Alibaba Group
HEARTS: Benchmarking LLM Reasoning on Health Time Series
Feb 25, 2026Sirui Li, Shuhan Xiao, Mihir Joshi +4LLM Evaluation Time Series Reasoning
University of California, Los Angeles, 2Google Research · 2Google Research
Adaptive Time Series Reasoning via Segment Selection
Feb 20, 2026Shvat Messica, Jiawen Zhang, Kevin Li +2Time Series Reasoning Time Series QA
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA · The Hong Kong University of Science and Technology (Guangzhou) · Massachusetts Institute of Technology +1
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
Feb 15, 2026Nicolas Zumarraga, Thomas Kaar, Ning Wang +12Time Series Reasoning Time Series QA
1Agentic Systems Lab, ETH Zurich · 2Stanford University · 3Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich +4

Oct 5, 2026, cs.AIJ/K move · Enter open · S save

Do Time-Series QA Systems Read the Time Series? Evidence Use and Reasoning Reliability

Zhuomin Chen, Jingchao Ni, Xu Zheng, Janki Bhimani+3

Florida International University · University of Houston · NEC Laboratories America+1

Time Series Reasoning Time Series QA Factual Consistency Evaluation Sensitivity Analysis

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In recent years, time-series question answering (QA) systems have made significant progress. However, generating a correct answer does not show whether retaining the supplied numerical series improves task performance, nor whether the prediction is sensitive to changes in that input. While some systems provide rationales, answer accuracy also does not show whether their numerical claims are grounded in the supplied series or whether the stated inference is valid. In this work, we focus on evaluating four time-series QA systems: TimeOmni-1, ChatTS, TimeOmni-VL, and Time-MQA. First, for three systems with released evaluation data, we reproduce their reported results and compare the performance of the systems with their backbones. Then, we introduce a benchmark named COMMON-TSQA, which collects public evaluation datasets from existing time-series benchmarks and unifies their sample representation, task definitions, and answer schemas, while evaluating each system through its own interface under common evaluation criteria. The evaluation uses the original condition and six interventions while keeping the question and target fixed. Our analysis shows that aggregate performance alone can obscure how systems use numerical evidence. Similar task-level scores can arise despite substantial changes in individual predictions. Some interventions induce simple fallback behavior rather than preserved task ability. We also evaluate rationales for factual grounding, inference validity, and consistency with the final answer. We find that rationales often contain time-series claims unsupported by the input. Moreover, the rationale audit shows that agreement between a rationale and its final answer can coexist with incorrect numerical descriptions or invalid intermediate inferences.

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