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A toolkit for conducting machine learning tasks with time series data

Home Page: https://aeon-toolkit.org/

License: BSD 3-Clause "New" or "Revised" License

Shell 0.02% Python 99.96% Dockerfile 0.01%

aeon's Introduction

aeon logo

⌛ 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 v0.5.0. You can view the full changelog here.

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

Overview
CI/CD github-actions-release github-actions-main github-actions-nightly docs-main docs-main !codecov
Code !pypi !conda !python-versions !black license binder
Community !slack !linkedin !twitter

⚙️ 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 and classification.

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

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']

💬 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

aeon's People

Contributors

mloning avatar fkiraly avatar tonybagnall avatar matthewmiddlehurst avatar aiwalter avatar sajaysurya avatar goastler avatar jasonlines avatar viktorkaz avatar chrisholder avatar lmmentel avatar hadifawaz1999 avatar danbartl avatar ltsaprounis avatar achieveordie avatar rnkuhns avatar miraep8 avatar patrickzib avatar ciaran-g avatar thayeylolu avatar guzalbulatova avatar prockenschaub avatar eenticott-shell avatar jesellier avatar bethrice44 avatar khrapovs avatar kishmanani avatar katiebuc avatar lovkush-a avatar aurumnpegasus avatar

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