  In multivariate time series (MTS) classification, finding the important
features (e.g., sensors) for model performance is crucial yet challenging due
to the complex, high-dimensional nature of MTS data, intricate temporal
dynamics, and the necessity for domain-specific interpretations. Current
explanation methods for MTS mostly focus on time-centric explanations, apt for
pinpointing important time periods but less effective in identifying key
features. This limitation underscores the pressing need for a feature-centric
approach, a vital yet often overlooked perspective that complements
time-centric analysis. To bridge this gap, our study introduces a novel
feature-centric explanation and evaluation framework for MTS, named CAFO
(Channel Attention and Feature Orthgonalization). CAFO employs a
convolution-based approach with channel attention mechanisms, incorporating a
depth-wise separable channel attention module (DepCA) and a QR
decomposition-based loss for promoting feature-wise orthogonality. We
demonstrate that this orthogonalization enhances the separability of attention
distributions, thereby refining and stabilizing the ranking of feature
importance. This improvement in feature-wise ranking enhances our understanding
of feature explainability in MTS. Furthermore, we develop metrics to evaluate
global and class-specific feature importance. Our framework's efficacy is
validated through extensive empirical analyses on two major public benchmarks
and real-world datasets, both synthetic and self-collected, specifically
designed to highlight class-wise discriminative features. The results confirm
CAFO's robustness and informative capacity in assessing feature importance in
MTS classification tasks. This study not only advances the understanding of
feature-centric explanations in MTS but also sets a foundation for future
explorations in feature-centric explanations.
