Expert-Guided Forecast Editing for Time-Series Foundation Models

2026-07-22 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new framework, DEFT (Expert-Guided Forecast Editing), addresses the limitation of time-series foundation models whose forecasts are fixed and cannot directly incorporate expert feedback. DEFT enables expert-guided forecast revision under tight query budgets by balancing exploitation of the foundation model's predictive distribution with structured exploration. It achieves this by first leveraging predictive samples in a decomposed trend-seasonal space, then refining forecasts component-wise. DEFT queries experts on complete trajectories but reuses scores for individual trend and seasonal components, providing structured feedback while keeping the foundation model frozen. Compared against direct search approaches like best-of-N, cross-entropy methods, and Bayesian optimization across 78 datasets, three time-series foundation models, four feedback types, and seven query budgets, DEFT consistently improved expert guidance effectiveness. A molecular-dynamics case study further supports its applicability to physically grounded feedback.

Key takeaway

For AI Scientists and Research Scientists tasked with refining time-series foundation model forecasts using limited expert feedback, DEFT offers a significantly more effective approach than traditional direct search methods. By intelligently balancing the exploitation of model predictions with structured exploration in a decomposed space, DEFT maximizes the value of each expert query. You should consider integrating DEFT's principles to achieve superior forecast accuracy and efficiency in your expert-guided revision workflows, especially when operating under strict budget constraints.

Key insights

DEFT improves expert-guided time-series forecast editing by balancing foundation model exploitation with structured, component-wise exploration under tight query budgets.

Principles

• Balance prior exploitation with structured exploration.
• Decompose forecasts into trend and seasonal components.
• Reuse expert scores for component-level feedback.

Method

DEFT exploits foundation model samples in a decomposed trend-seasonal space, then explores around them via component-wise refinement. It queries experts on complete trajectories and reuses scores for individual trend and seasonal components.

In practice

• Apply DEFT to enhance time-series forecasting.
• Extend DEFT principles to physical feedback systems.
• Improve expert guidance under tight query budgets.

Topics

• Time-Series Foundation Models
• Forecast Editing
• Expert Guidance
• DEFT Framework
• Bayesian Optimization
• Molecular Dynamics

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.