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
The proliferation of complex AI models across critical time-series dependent applications necessitates more robust and reliable explainability methods to build trust and ensure compliance.
Improved explainability for time-series forecasting models is crucial for sectors like finance, weather, and healthcare, enabling better decision-making and risk management in AI-driven systems. This research addresses a fundamental limitation in current XAI methods that inaccurately assume independence of time-series features.
The introduction of KARMA provides a new framework for generating global explanations that account for temporal dependencies, offering more accurate insights into why a time-series model makes specific predictions. This could lead to wider adoption of AI in sensitive time-series domains.
- · AI/ML developers
- · Financial institutions
- · Healthcare providers
- · Regulatory bodies
- · Companies relying on black-box time-series AI
- · Less sophisticated XAI methods
More transparent and trustworthy AI applications for time-series data become possible, enhancing adoption in critical sectors.
Increased regulatory scrutiny and requirements for explainability in AI models, particularly in industries with high-stakes temporal predictions.
New competitive landscape emerges where explainability becomes a key differentiator for AI solutions, driving further innovation in XAI research and tooling.
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Read at arXiv cs.LG