Amadeo Tunyi

2 papers indexed

arxivcs.LGcs.AI2026-06-25

Global Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations

While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent. This assumption ignores the fundamental property of temporal dependence and can…

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arxivcs.LGcs.AI2026-06-25

The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations

Amadeo Tunyi

Explainability methods for time series models predominantly produce flat attribution scores: they quantify the direct influence of a feature at a timestamp by a scalar. We prove that the dominant failure mode of such methods is not the scalar format itself but a fundamental compu…

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