GitHub / hanyuanz2000 / Sparse-Gaussian-Process-for-Missing-Heart-Rate-Data-Imputation
Explores the application of Gaussian Process (GP) and sparse GP algorithms to handle missing heart rate time series dataset. Our findings emphasize the importance of kernel selection, specifically the RBF kernel, and the careful tuning of hyperparameters to achieve optimal performance in imputation tasks
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PURL: pkg:github/hanyuanz2000/Sparse-Gaussian-Process-for-Missing-Heart-Rate-Data-Imputation
Stars: 2
Forks: 1
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 61 MB
Dependencies parsed at: Pending
Created at: over 2 years ago
Updated at: about 1 year ago
Pushed at: about 2 years ago
Last synced at: about 1 month ago
Topics: gaussian-processes, gpytorch, machine-learning, timeseries