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
arXiv:2610.19725v1 Announce Type: new
Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for m...
By Haoxin Liu, Deqing Fu, Lucheng Fu, Yiqiao Jin, Rushi Qiang, Harshavardhan Kamarthi, B. Aditya Prakash
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...
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
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: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: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
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
CastClaw is an interactive forecasting system that combines an autonomous forecasting loop with a forecasting-oriented harness. Users define and refine forecasting tasks in natural language via command-line or Web interfaces, while an internal large language model agent selects tool actions or requests human feedback, using checked results to guide the forecasting process. The system integrates tool execution, state management, and validation, and has been evaluated on five public electricity‑price datasets, outperforming 16 baselines in point‑estimate MSE and MAE, with case studies on Nord Pool and North China electricity‑load data.
By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen
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