A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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...
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: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...
arXiv:2608. 03339v1 Announce Type: new Abstract: Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise 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:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.