aeon
is an open source toolkit for learning from time series. It is compatible with
scikit-learn and provides access to the very latest
algorithms for time series machine learning, in addition to a range of classical
techniques for learning tasks such as forecasting and classification.
We strive to provide a broad library of time series algorithms including the latest advances, offer efficient implementations using numba, and interfaces with other time series packages to provide a single framework for algorithm comparison.
The latest aeon
release is v0.4.0
. You can view the full changelog
here.
- The deprecation policy is currently suspended, be careful with the version bounds used when including aeon as a dependency.
- The policy will return at a future point, but in the mean time the suspension allows us to quickly develop and maintain the package in the forking transition period.
Our webpage and documentation is available at https://aeon-toolkit.org.
Overview | |
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CI/CD | |
Code | |
Community |
aeon
requires a Python version of 3.8 or greater. Our full installation guide is
available in our documentation.
The easiest way to install aeon
is via pip:
pip install aeon
Some estimators require additional packages to be installed. If you want to install the full package with all optional dependencies, you can use:
pip install aeon[all_extras]
The best place to started for all aeon
packages is our getting started guide.
Below we provide a quick example of how to use aeon
for forecasting and
classification.
import pandas as pd
from aeon.forecasting.trend import TrendForecaster
y = pd.Series([20.0, 40.0, 60.0, 80.0, 100.0])
>>> 0 20.0
>>> 1 40.0
>>> 2 60.0
>>> 3 80.0
>>> 4 100.0
>>> dtype: float64
forecaster = TrendForecaster()
forecaster.fit(y) # fit the forecaster
>>> TrendForecaster()
pred = forecaster.predict(fh=[1, 2, 3]) # forecast the next 3 values
>>> 5 120.0
>>> 6 140.0
>>> 7 160.0
>>> dtype: float64
import numpy as np
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
X = [[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)
[[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length,
[[8, 7, 6, 5, 4, 4]]]
y = ['low', 'low', 'high'] # class labels for each sample
X = np.array(X)
y = np.array(y)
clf = KNeighborsTimeSeriesClassifier(distance="dtw")
clf.fit(X, y) # fit the classifier on train data
>>> KNeighborsTimeSeriesClassifier()
X_test = np.array(
[[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
y_pred = clf.predict(X_test) # make class predictions on new data
>>> ['low' 'high' 'high']
Type | Platforms |
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🐛 Bug Reports | GitHub Issue Tracker |
✨ Feature Requests & Ideas | GitHub Issue Tracker & Slack |
💻 Usage Questions | GitHub Discussions & Slack |
💬 General Discussion | GitHub Discussions & Slack |
🏭 Contribution & Development | Slack |