This study proposes a diverse family of additive models that extend Generalized Additive Models to achieve both interpretability and predictive performance in forecasting. While preserving the stability of traditional GAMs, we develop Feature-wise Additive Models, Gradient Boosting Additive Models, and TabNet-based Neural Additive Models (TabNAM).
forecasting ensemble-learning generalized-additive-models interpretable-ml explainable-ai tabnet tabular-deep-learning neural-additive-models tabnam boosting-additive-models feature-wise-additive-models
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Updated
Mar 15, 2026 - Python