TimeRSI is a self‑improving deployment system for time‑series foundation models that automates the creation and refinement of forecasting pipelines. A coding agent translates task descriptions into executable changes, tests each revision on historical data, and uses error feedback to guide further revisions. During live deployment, an adaptive selector chooses the best pipeline based on completed forecasts under similar conditions, achieving superior performance on 198 tasks compared to all other submissions.
By Haoxin Liu, Deqing Fu, Lucheng Fu, Yiqiao Jin, Rushi Qiang, Harshavardhan Kamarthi, B. Aditya Prakash
Time-series foundation models provide strong zero-shot forecast, but deployment for real-world applications still requires task-specific designs for model inputs and outputs. Direct inference can leav...
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...
arXiv:2609.24862v1 Announce Type: new
Abstract: Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning s...
By Yifan Hu, Xilin Dai, Zhiyuan Qu, Yiding Liu, Zewei Dong, Jiang-ming Yang, Qiang Xu
arXiv:2606. 18733v1 Announce Type: cross Abstract: Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection.
By Qiao Zhao, JianYing Qu, Jun Zhang, Yehua Yang, Hanwen Du, Zhongkai Sun
arXiv:2608.30976v1 Announce Type: new
Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...
By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen
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...
By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng
Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time...
arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.
By Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang
arXiv:2605.16358v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remain...
By Mingtian Tan, Mihir Parmar, Palash Goyal, Chun-Liang Li, Nanyun Peng, Thomas Hartvigsen, Jinsung Yoon, Tomas Pfister
MetaCaster is a meta-harness-optimized multi-agent framework that enables few-shot learning for lightweight time series forecasters. It uses agentic data generation to automatically train specialized forecasters from only a few examples and textual contexts, positioning agents as intermediary engineers rather than direct forecasters. Experiments on 18 datasets and 23 lightweight forecasters show that MetaCaster achieves data and computational efficiency while maintaining high forecasting quality.
By ChengAo Shen, Wenchao Yu, Fangyu Wu, Dongjin Song, Hanghang Tong, Dongsheng Luo, Wei Cheng, Haifeng Chen, Jingchao Ni
CastFlow is a role‑specialized agentic forecasting framework that combines general‑purpose tool reasoning with specialized temporal forecasting. It uses an Experience Memory to guide a frozen LLM in acquiring evidence via a multi‑view toolkit, then a lightweight specialized LLM refines predictions through evidence‑grounded reasoning. The two‑stage training strategy—supervised fine‑tuning followed by reinforcement learning with verifiable rewards—optimizes the specialized model based on final forecasting outcomes, achieving superior performance on nine out of ten real‑world benchmarks.
By Bokai Pan, Mingyue Cheng, Xiaoyu Tao, Zhiding Liu, Shuo Yu, Yuchong Wu, Qi Liu, Defu Lian, Enhong Chen