Time series anomaly detection
Project description
Welcome to Gripa
Introduction
Gripa is a python package for time series anomaly detection. The name of Gripa is taken from Scandinavian languages, which means grab or catch or seize, and not meant as Flu in Spanish :smile:.
Why Gripa?
- Gripa is light-weight. Try Gripa before you build LSTM or Transformer for your time series anomaly detection.
- Gripa is accurate. Gripa has two available algorithms that perform very well in detecting anomalies on your time series data.
- Gripa is comprehensive. Gripa can detect three types of anomaly: global anomaly, contextual anomaly, and level-shift.
Quick Start
Installation
Python 3.9
or higher is required.
python -m pip install gripa
Usage
# Load data
from gripa import Gripa
detector = Gripa()
anomalies = detector.fit_predict(data)
API Reference
There is only one module, gripa.Gripa
, that can be used for detecting anomalies in time series.
class gripa.Gripa(window_size=11, algorithm="hpf", threshold=3)
Parameters
window_size
: int or float, default=11algorithm
: {"hpf", "ssa"}, default="hpf"threshold
: float, default=3
Attributes
anomaly_score
: score for labelling anomaly
Methods
fit(X)
: train the Gripa model, which can generate attributeanomaly_score
fit_predict(X)
: train and generate anomaly labels (True
orFalse
)
Contact
Hamid Dimyati - hamid.dimyati@outlook.com
Project details
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