Graph-Machine-Learning-Group/grin
Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
This project helps you fill in missing data points within complex, interconnected time series, such as readings from a network of sensors. You input historical time series data, which might have gaps due to sensor malfunctions or network issues, and it outputs a complete dataset where those gaps are intelligently filled. This is designed for data scientists or researchers who work with sensor networks, climate data, or traffic flow information.
177 stars. No commits in the last 6 months.
Use this if you need to accurately reconstruct missing observations in multivariate time series data that comes from sensor networks or other systems with known relationships between data points.
Not ideal if your data is not structured as a network of interconnected time series or if you only have simple, univariate time series with missing values.
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Language
Python
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Last pushed
Jun 22, 2022
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