TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

Key points

• 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.1
• Direct inference can leave domain knowledge and re- curring forecast errors unaddressed, while tai- lored pipelines require substantial human ex- pertise and effort.1
• A cod- ing agent translates task descriptions into ex- ecutable pipeline changes.1
• Further experiments verify the generalization with different coding agents and forecasting models.1

Sources (2)

• [1]TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:48 AM

  We introduce TIMERSI, a self-improving agentic deployment system around frozen forecasting 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.

• [2]TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models
  Hugging Face Daily Papers · Oct 15, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.