m4urin/temporal-causal-discovery
Researching causal relationships in time series data using Temporal Convolutional Networks (TCNs) combined with attention mechanisms. This approach aims to identify complex temporal interactions. Additionally, we're incorporating uncertainty quantification to enhance the reliability of our causal predictions.
This project helps data scientists and researchers identify complex cause-and-effect relationships within time series data. It takes raw time series measurements and outputs a causal matrix that shows which variables influence others over time, including nuanced, non-linear interactions. It's designed for professionals working with dynamic data where understanding temporal causality is critical for decision-making.
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Use this if you need to understand intricate, time-delayed causal links in your time series data, especially when simple additive models might miss complex interactions.
Not ideal if your primary goal is to identify only the most obvious, direct causal relationships, as simpler additive models might be more robust for those cases.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Jun 14, 2024
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