A curated collection of papers on Agentic Time Series.
Current time series analysis paradigms face fundamental limitations:
| ❌ Traditional Paradigm | ✅ Agentic Paradigm |
|---|---|
|
Fixed, Single-step, Single-direction, Limited Static and one-shot — once trained, the model produces predictions in a single forward pass without any ability to revisit, refine, or adapt its reasoning process. |
Adaptive, Interactive, Closed-loop, Cyclic Iterative perception, planning, action, reflection, and memory accumulation — enabling closed-loop and self-adaptive time series analysis. |
We formulate time series analysis as a sequential decision optimization and path search problem — moving beyond static model-centric prediction toward multi-step reasoning, tool-augmented decision-making, and experience-driven evolution.
| Paper | Highlight | Links | |
|---|---|---|---|
| 1 | Garza A, Rosillo R. TimeCopilot |
通用时间序列分析智能体。 | |
| 2 | Zhao H, Zhang X, Wei J, et al. Timeseriesscientist: A General-Purpose AI Agent for Time Series Analysis |
通用时间序列分析智能体。 |
| Paper | Highlight | Links | |
|---|---|---|---|
| 1 | Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao. AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning |
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If you find this collection helpful, please consider citing our position paper:
@article{cheng2026atsf,
title = {Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting},
author = {Cheng, Mingyue and Tao, Xiaoyu and Liu, Qi and Guo, Ze and Chen, Enhong},
journal = {arXiv preprint arXiv:2602.01776},
year = {2026}
}