M-Nauta/TCDF
Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series
This tool helps you understand how different factors in a complex system influence each other over time. You provide raw time series data, like stock prices or sensor readings, and it generates a causal map showing which time series affect others and with what delay. This is useful for anyone who needs to make data-driven decisions or predictions by understanding the underlying causes.
531 stars. No commits in the last 6 months.
Use this if you have multiple time series datasets and want to uncover the cause-and-effect relationships between them, including any time delays.
Not ideal if your data is not in a time series format or if you are looking for simple correlations rather than causal links.
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531
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Jupyter Notebook
License
GPL-3.0
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Last pushed
Oct 01, 2021
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