WenjieDu / Awesome_Imputation
Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list about applying neural networks to impute incomplete time series containing NaN missing values/data
Why this job: name: awesome
What it does
The repository for the paper TSI-Bench: Benchmarking Time Series Imputation from PyPOTS Research. The code and configurations for reproducing the experimental results in the paper are available under the folder benchmark_code. The README file here maintains a list of must-read papers on time-series imputation, and a collection of time-series imputation toolkits and resources.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 9 months ago
- Stars
- 428
- On GitHub
- Licence
- BSD-3-Clause
- Allows business use
- Downloads for
- None listed
- No release files
- Latest tag
- imputation_survey
- Released 2 years ago
- Built with
- Python
- 80% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 90% closed
- 1 still open
- Pull requests
- 18 merged
- 1 waiting
- Releases
- 1
- 0 in the last year
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Python 80%
- Jupyter Notebook 17%
- Shell 3%
Topics
- benchmark
- data-mining
- deep-learning
- imputation
- machine-learning
- missing-data
- missing-values
- neural-network
- probablistic
- survey
- time-series
- interpolation
Project record
- JobLists and guides
- StatusListed
- Commits83
- Forks47
- Watching6
- Stars in 28 days+1
- CreatedDec 12, 2023
- First seen by usNov 7, 2026
- CheckedNov 8, 2026