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

arXiv:2606.27599v1 Announce Type: new Abstract: 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 lead to explanations that violate the sequential and causal structure of the data. We introduce \textsc{KARMA}, a method for explaining time-series predictors by constructing a Markov surrogate model that captures the temporal dependencies learned by the predictor. Our ap

Source: arXiv cs.LG — read the full report at the original publisher.

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