zichuan-liu/ContraLSP
[ICLR'24] Official PyTorch Implementation of ContraLSP
This project helps data scientists and researchers understand why a machine learning model made a specific decision on time series data, such as a prediction or classification. It takes your existing time series data and a trained model, then highlights the most influential parts of the time series that led to the model's output. This allows you to gain insights into complex time series behaviors.
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Use this if you need to explain the reasoning behind a machine learning model's output when working with time-dependent data.
Not ideal if you are looking for a tool to build or train time series models from scratch rather than explain existing ones.
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Python
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Last pushed
Apr 12, 2024
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