SEA-Net: A Small and Inherently Interpretable Neural Network for Time Series Classification

Traditional time-series classification methods often operate as black boxes, providing a class prediction without indicating which time point or time interval within the series led to that prediction. To improve temporal interpretability, MILLET (Multiple Instance Learning with Temporal Explanation), which combines an InceptionTime encoder with Conjunctive Pooling, provides both classification and temporal explanations. However, MILLET contains approximately $424$ K parameters, making it unsuitable for deployment on resource-constrained micro-controllers, and it achieves an overall time-series accuracy of $0.888$ and an explanation score of $0.681$ NDCG@$n$. We therefore propose SEA-NET, a lightweight architecture using small multi-scale depthwise-separable convolutional TSC encoders and improved Multiple Instance Learning (MIL) pooling heads. Our bottleneck encoder with Top-$k$ pooling achieves $0.931$ accuracy and $0.765$ NDCG@$n$ with only $41$ K parameters. Another variant, using a gated encoder with Top-$k$ pooling, achieves $0.955$ accuracy and $0.750$ NDCG@$n$ with $62$ K parameters. These results demonstrate that lightweight architectures can maintain strong classification performance and temporal explanations while substantially reducing model size. The proposed variants address specific limitations of the pooling strategy used by , providing a practical direction for interpretable time-series classification on resource-constrained edge devices.