Hugging Face Trending Papers
A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
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arXiv Machine Learning
Sep 1
A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
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...
arXiv Machine Learning
Oct 19
CastClaw: A Human-in-the-Loop Autonomous Agent for Interactive Time Series Forecasting
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.
arXiv Machine Learning
Oct 15
TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
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...
Hugging Face Trending Papers
Oct 14
TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
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...
arXiv AI
Jun 2
Bridging the Last Mile of Time Series Forecasting with LLM Agents
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.
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...