arXiv Machine Learning
Jun 5
Harnessing Generalist Agents for Contextualized Time Series
arXiv:2606. 05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling.
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.
Related stories
arXiv AI
Aug 17
TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments
arXiv:2608. 14270v1 Announce Type: new Abstract: Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions.
arXiv AI
Jun 11
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
arXiv:2509. 11575v3 Announce Type: replace Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer.
arXiv AI
Jun 16
Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning
arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.
arXiv AI
Sep 11
KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
KairosAgent is an agentic framework that combines a large language model (LLM) reasoner with a time series foundation model (TSFM) forecaster to tackle cross‑domain multimodal time series forecasting. It dynamically invokes analytical tools to improve the LLM’s numerical comprehension and semantic reasoning, then fuses the reasoning outcomes into the TSFM pipeline for more accurate predictions. The approach is further enhanced by a curated large‑scale trajectory corpus and a reinforcement learning paradigm with multi‑turn refinement and turn‑level credit assignment, achieving superior zero‑shot forecasting performance.
arXiv AI
Aug 5
CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.
arXiv AI
Aug 25
Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs
arXiv:2506.10630v4 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techni...
arXiv AI
Jun 4
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.
arXiv AI
Sep 15
TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for qu...
arXiv AI
Aug 25
LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...
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
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.