A simple-to-use Python package for the development and analysis of time series anomaly
detection techniques. Here we describe the main usage of dtaianomaly, but be sure to
check out the documentation
for more information.
The preferred way to install dtaianomaly is via PyPi. See the documentation
for more options.
pip install dtaianomaly
The three key features of dtaianomaly are as follows:
- State-of-the-art time series anomaly detection via a simple API. Learn more!
- Develop custom models for anomaly detection. Learn more!
- Quantitative evaluation of time series anomaly detection. Learn more!
Below code shows a simple example of dtaianomaly, which is part of the
anomaly detection notebook. More examples
are provided in the other notebooks and in the
documentation.
from dtaianomaly.data import demonstration_time_series
from dtaianomaly.preprocessing import MovingAverage
from dtaianomaly.anomaly_detection import MatrixProfileDetector
# Load the data
X, y = demonstration_time_series()
# Preprocess the data using a moving average
preprocessor = MovingAverage(window_size=10)
X_, _ = preprocessor.fit_transform(X)
# Fit the matrix profile detector on the processed data
detector = MatrixProfileDetector(window_size=100)
detector.fit(X_)
# Compute either the decision scores, specific to the detector, or the anomaly probabilities
decision_scores = detector.decision_function(X_)
anomaly_probabilities = detector.predict_proba(X_)
The goal of dtaianomaly is to be community-driven. All types of contributions
are welcome. This includes code, but also bug reports, improvements to the documentation,
additional tests and more. Check out the documentation
to find more information about how you can contribute!
Copyright (c) 2023-2025 KU Leuven, DTAI Research Group
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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