williamgilpin/fnn
Embed strange attractors using a regularizer for autoencoders
This tool helps scientists and researchers uncover hidden variables and understand complex system dynamics from experimental time series data. You input a single or few-dimensional time series, such as temperature readings, ECG signals, or sensor data, and it outputs a higher-dimensional "embedding" that reveals the underlying behavior of the system. This is useful for anyone studying systems that generate complex, seemingly chaotic data.
135 stars. No commits in the last 6 months.
Use this if you have a time series and suspect there are more fundamental, unobserved variables driving the system's behavior that you want to reconstruct.
Not ideal if your goal is simple forecasting or anomaly detection on a time series, rather than understanding its underlying dynamical structure.
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135
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33
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Jupyter Notebook
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Last pushed
Jun 09, 2021
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