The function 'missImputeTS' in this package is used to impute timeseries missing values particularly in the case of mixed-type data.It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It can be run in parallel to save computation time.
Project description
The author of this package has not provided a project description
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file missImputeTS-0.0.0.21-py3-none-any.whl.
File metadata
- Download URL: missImputeTS-0.0.0.21-py3-none-any.whl
- Upload date:
- Size: 47.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/4.6.3 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.0 CPython/3.6.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b0a844ef3c7222ed6d00b6ed2fdaae82a03b1878aa1c86f4d39102647e2b0aa0
|
|
| MD5 |
d8e6b34e39c145439786199492c19b9c
|
|
| BLAKE2b-256 |
37f75212ebf964de3f5bd98ccd2d76fb587ca840475d218b49ebfd75da9963a5
|
