The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations
A new preprint demonstrates that widely used attribution methods for time series models, such as SHAP, fundamentally conflate direct and mediated temporal dependencies due to a computational mismatch. The authors introduce the concept of DAG-faithfulness and prove that standard and time-series-aware attribution methods fail to satisfy this property, highlighting a key limitation in current explainability techniques for time series data.
Why it matters: This work exposes a significant flaw in popular methods for explaining time series models, raising concerns about the reliability of AI explanations in critical domains like finance, healthcare, and climate science.
Full story at: arXiv Machine Learning ↗