flaviagiammarino/brits-tensorflow
TensorFlow implementation of BRITS model for multivariate time series imputation with bidirectional recurrent neural networks.
This tool helps data scientists and machine learning engineers prepare multivariate time series data for analysis by filling in gaps. It takes your raw time series, which might have missing observations, and outputs a complete time series where those gaps are intelligently estimated. This is useful for anyone working with sensor data, financial logs, or any sequential measurements where some data points might be absent.
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Use this if you have time series data with multiple related variables where some data points are missing, and you need a robust way to estimate those missing values.
Not ideal if you are working with single time series, or if your data gaps are very long and frequent, as the model's ability to accurately infer missing information will be limited.
Stars
13
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2
Language
Python
License
MIT
Category
Last pushed
Apr 15, 2024
Commits (30d)
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