This is a project that automatically identifies the outliers from a given dataset
Project description
outlier_detection
Detect time-series anomalies for day-level dataset
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 Distribution
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 outlier_detection_nke-0.0.1.tar.gz.
File metadata
- Download URL: outlier_detection_nke-0.0.1.tar.gz
- Upload date:
- Size: 14.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
90a71a97a8d83bce98e9ee4308cd665dcda8196a01b6064cda2dcc8a64b31438
|
|
| MD5 |
dccd1492c6e2a67879fcd2c0f2983c28
|
|
| BLAKE2b-256 |
572adafbb36d0f18a77eccd5afc7411f3fe01f6da64f6043cfd5374694c812fa
|
File details
Details for the file outlier_detection_nke-0.0.1-py3-none-any.whl.
File metadata
- Download URL: outlier_detection_nke-0.0.1-py3-none-any.whl
- Upload date:
- Size: 14.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3364efee1b76c0e0f77a18e6329d0b8b4f8cbb6e8ebad87de6d45fae0d76ccc8
|
|
| MD5 |
a9dbe0eafd0773d595eca6a77d0bcddb
|
|
| BLAKE2b-256 |
fcf4dda2a4c324109085aa10794b6fc824e2713e3d56616a555894f3c94c583b
|
