Interpretability in Deep Time Series Models Demands Semantic Alignment

ArXi:2602.02239v2 Announce Type: replace Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, existing interpretability approaches in the field keep focusing on explaining the internal model computations, without addressing whether they align or not with how a human would reason about the studied phenomenon.