Timeseries Imputation
This page serves as a resource for a collection of publications and articles related to data imputation with a focus on timeseries imputation.
Imputation is a method by which synthetic data can be produced to fill gaps in a dataset. Some good starting articles include:
- 6 Different Ways to Compensate for Missing Values In a Dataset: a simple and gentle introduction into some of the more common ways to impute data.
This work was supported by Whole Communities—Whole Health, a research grand challenge at the University of Texas at Austin.
Please direct any questions about this repository/project to Hagen Fritz at [email protected].