Scholarlink

<p>Global feature importance (GFI) methods are essential tools for interpreting machine learning models, yet their application in more complex forecasting tasks involving multiple time series can be challenging. This study focuses on tree-based ensemble models applied to multi-series product demand forecasting. To evaluate the different GFI methods, using controlled simulated datasets with controlled dependencies on lagged values and external demand drivers, we compare model-specific and model-agnostic global importance methods, including Shapley values, permutation importance, and tree-specific gain- and split-based importance. Our analysis focuses on uncovering pitfalls in applying these methods, including problems introduced by auto-correlation and feature scaling. This work contributes practical guidance for practitioners seeking to apply these methods in real-world forecasting scenarios and to leverage explainability methods for informed decision-making.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards Explainable Decision Support in Demand Forecasting: Global Feature Importance for Multi-Series Tree-Based Models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

查看全文
  • Mátyás Kuti-Kreszács,
  • Laura Dioşan,
  • Zalán Bodó

摘要

Global feature importance (GFI) methods are essential tools for interpreting machine learning models, yet their application in more complex forecasting tasks involving multiple time series can be challenging. This study focuses on tree-based ensemble models applied to multi-series product demand forecasting. To evaluate the different GFI methods, using controlled simulated datasets with controlled dependencies on lagged values and external demand drivers, we compare model-specific and model-agnostic global importance methods, including Shapley values, permutation importance, and tree-specific gain- and split-based importance. Our analysis focuses on uncovering pitfalls in applying these methods, including problems introduced by auto-correlation and feature scaling. This work contributes practical guidance for practitioners seeking to apply these methods in real-world forecasting scenarios and to leverage explainability methods for informed decision-making.