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https://github.com/shenxiangzhuang/pysesd
Time Series Seasonal Extreme Studentized Deviate(S-ESD) in Python
https://github.com/shenxiangzhuang/pysesd
anomaly-detection esd extreme-value-statistics python s-h-esd statistics time-series twitter
Last synced: 24 days ago
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Time Series Seasonal Extreme Studentized Deviate(S-ESD) in Python
- Host: GitHub
- URL: https://github.com/shenxiangzhuang/pysesd
- Owner: shenxiangzhuang
- License: mit
- Created: 2023-06-19T08:49:17.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-27T02:09:02.000Z (2 months ago)
- Last Synced: 2024-08-28T01:40:22.378Z (2 months ago)
- Topics: anomaly-detection, esd, extreme-value-statistics, python, s-h-esd, statistics, time-series, twitter
- Language: Python
- Homepage: https://datahonor.com/pysesd/
- Size: 1.24 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[Py]S-ESD
Seasonal Extreme Studentized Deviate(S-ESD) in Python.
# About
Seasonal Extreme Studentized Deviate(S-ESD) in Python.
# Installation
Get latest version from PyPI:
```shell
pip install pysesd
```
# Getting started
## Simple example
```python
from pysesd.dataset import load_synthetic_ts
from pysesd.sesd import SESD
ts = load_synthetic_ts()
sesd = SESD(alpha=0.05, hybrid=False, max_outliers=2)
outliers = sesd.fit(ts)
sesd.plot(save=True, fig_dir="../figures", fig_name="simple.png")
```
## Twitter example
```python
from pysesd.dataset import load_twitter_ts
from pysesd.sesd import SESD
ts = load_twitter_ts()
sesd = SESD(alpha=0.05, hybrid=True, max_outliers=int(len(ts) * 0.02))
outliers = sesd.fit(ts)
sesd.plot(save=True, fig_dir="../figures", fig_name="twitter.png")
```
# References
- [twitter/AnomalyDetection](https://github.com/twitter/AnomalyDetection)