⌛ Welcome to aeon

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 v0.8.1. You can view the full changelog here.

Our webpage and documentation is available at https://aeon-toolkit.org.

The following modules are still considered experimental, and the deprecation policy does not apply:

anomaly_detection, benchmarking, segmentation, similarity_search, testing, transformations/series, visualisation

Overview
CI/CD
Code
Community

⚙️ Installation

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]

Instructions for installation from the GitHub source can be found here.

⏲️ Getting started

The best place to get started for all aeon packages is our getting started guide.

Below we provide a quick example of how to use aeon for forecasting, classification and clustering.

Forecasting

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

Classification

It's worth mentioning that the classifier used in the example can easily be swapped out for a regressor, and the labels for numeric targets. This flexibility allowing for seamless adaptation to different tasks and datasets while preserving API consistency.

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']

Clustering

import numpy as np
from aeon.clustering import TimeSeriesKMeans

X = np.array([[[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]]])

clu = TimeSeriesKMeans(distance="dtw", n_clusters=2)
clu.fit(X)  # fit the clusterer on train data
>>> TimeSeriesKMeans(distance='dtw', n_clusters=2)

clu.labels_ # get training cluster labels
>>> array([0, 0, 1])

X_test = np.array(
    [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
clu.predict(X_test)  # Assign clusters to new data
>>> array([1, 0, 0])

💬 Where to ask questions

Type	Platforms
🐛 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