Explainability for Forecasting Task with Close-Up Option

Although machine learning-based forecasting models often function as opaque black boxes, explainability methods provide the essential tools required to foster trust in deep learning systems, optimize computational resources, and enhance model accuracy. Motivated by this, we propose a model-agnostic, local explainability method specifically designed for forecasting tasks. Our approach employs a hierarchical structure that is generated iteratively. From the outset, a small number of “interpretable components” that decompose the input are considered. In the inductive step, the methodology enables analysts to examine the data in greater detail, allowing for a deeper exploration of specific subcomponents whenever a higher resolution is required. This framework inherently provides control over computational costs by tuning the branching factor. The resulting explainability tree shares certain, but not all, properties of the explainability values provided by the SHAP-LIME framework; in this regard, we present some theoretical results. Furthermore, numerical experiments provided evidence that explanation coherence is fundamentally maintained between both methods. We explored the utility of this methodology on a real-world short-term load forecasting LSTM encoder–decoder model.