arXiv AI
Jun 2
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.
TSAQA: Time Series Analysis Question And Answering Benchmark
arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.
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arXiv AI
Aug 19
TSQueryBench: LLM-as-a-Judge for Time Series Explanations
TSQueryBench is a synthetic benchmark comprising 500 time‑series instances and 10 query types, each paired with correct, partially correct, and incorrect natural‑language explanations. The study evaluates six large language models on explanation generation, ranking, scoring, and anomaly detection, revealing that models often fail to generate numerically correct explanations yet can reliably identify or score correct ones. These findings suggest that rubric‑guided LLM evaluation is more dependable than generation for numerically grounded time‑series reasoning.
arXiv Machine Learning
Jun 4
It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.
arXiv AI
Aug 21
TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning
arXiv:2510. 03519v3 Announce Type: replace-cross Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery.
arXiv AI
Jun 2
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
arXiv:2606. 01300v1 Announce Type: cross Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry.
arXiv AI
Jun 2
AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
arXiv:2602. 08868v2 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional, detailed reasoning, which is vital for understanding complex time-series data.
arXiv AI
Jun 12
Representing Time Series as Structured Programs for LLM Reasoning
arXiv:2606. 12481v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong reasoning and instruction-following capabilities, making them potentially powerful tools for time-series analysis.
arXiv AI
Jun 16
TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
arXiv:2606. 16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making.
arXiv AI
Jun 18
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
arXiv:2606. 18986v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering.
arXiv AI
Jul 29
A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series
arXiv:2607. 25947v1 Announce Type: new Abstract: Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications.
arXiv Machine Learning
Sep 22
TAC-Time: Texts as Channels For Multimodal Time Series Forecasting
arXiv:2609.24156v1 Announce Type: cross Abstract: Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Re...
arXiv AI
Jul 14
CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series
arXiv:2607. 09880v1 Announce Type: cross Abstract: Clinical time series are central to patient monitoring, risk assessment, and clinical decision support.