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This paper adapts mechanistic interpretability techniques, including activation patching, attention saliency, and sparse autoencoders, to transformer-based time series classification models. The study aims to uncover the internal decision-making processes of these models, which are often obscured by their complexity. The experiments on benchmark datasets reveal causal graphs illustrating information flow, key attention heads, and temporal positions that drive correct classifications, along with interpretable latent features discovered via sparse autoencoders.
Uncovers the inner workings of transformer-based time series classifiers, revealing how specific attention heads and timesteps causally drive correct classifications.
Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes understanding their internal decision-making challenging. Existing explainability methods often focus on input-output attributions, leaving the internal mechanisms largely opaque. This paper addresses this gap by adapting various Mechanistic Interpretability techniques; activation patching, attention saliency, and sparse autoencoders, from NLP to transformer architectures designed explicitly for time series classification. We systematically probe the internal causal roles of individual attention heads and timesteps, revealing causal structures within these models. Through experimentation on a benchmark time series dataset, we construct causal graphs illustrating how information propagates internally, highlighting key attention heads and temporal positions driving correct classifications. Additionally, we demonstrate the potential of sparse autoencoders for uncovering interpretable latent features. Our findings provide both methodological contributions to transformer interpretability and novel insights into the functional mechanics underlying transformer performance in time series classification tasks.