AlaaSedeeq/Convolutional-GAIN
An implementation to Convolutional generative adversarial imputation networks for spatio-temporal missing data Nets Paper (Conv-GAIN)
This project helps fill in missing information in data that changes over time and space, like readings from environmental sensors or monitoring systems. It takes incomplete spatio-temporal datasets and intelligently estimates the missing values, providing a more complete picture. Researchers and engineers working with environmental modeling, urban planning, or remote sensing who need to analyze continuous, spatially distributed data would find this useful.
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Use this if you have gaps in your time-series data that also have a spatial component, such as weather station readings, pollution levels across a city, or sensor networks.
Not ideal if your missing data is purely tabular, non-sequential, or doesn't have a spatial relationship.
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Jupyter Notebook
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MIT
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Last pushed
Jun 10, 2022
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