ZLiu21/SoftShape
This is an official pytorch implementation for paper "Learning Soft Sparse Shapes for Efficient Time-Series Classification" (ICML-25, Spotlight).
This project helps researchers and data scientists classify time series data more efficiently and with clearer explanations. It takes your time series datasets and identifies key patterns (or "shapes") within them, providing highly accurate classifications along with interpretable insights into why a particular classification was made. It's designed for anyone working with sequential data who needs to understand the underlying drivers of classification outcomes.
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Use this if you need to classify complex time series data and require both high accuracy and clear interpretability for your results.
Not ideal if your primary goal is simple classification without any need for understanding the contributing patterns or shapes.
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Python
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Last pushed
Jun 09, 2025
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