Examples

This webpage contains the notebook examples for the aeon library. The examples are organised into categories based on module and functionality. The examples are designed to provide a comprehensive overview of a module, functionality or a specific algorithm.

Classification

Overview of Time Series Classification (TSC)

Time Series Classification

Convolution based TSC

Convolution based time series classification in aeon

Deep learning based TSC

Deep learning based time series classification in aeon

Dictionary based TSC

Dictionary based time series classification in aeon

Distance based TSC

Distance based time series classification in aeon

Feature based TSC

Feature based time series classification in aeon

Hybrid TSC

Hybrid time series classification in aeon

Interval based TSC

Interval based time series classification in aeon

Shapelet based TSC

Shapelet based time series machine learning

Early TSC

Early time series classification with aeon

Regression

Overview of Time Series Regression (TSR)

Time Series Regression

Clustering

Overview of Time Series Clustering (TSCL)

Time Series Clustering

Partitional TSCL

Partition based time series clustering in aeon

Using aeon Distances with sklearn Clusterers

Using aeon Distances with scikit-learn Clusterers

Transformation

Overview of Transformations

Transforming time series

Preprocessing time series

Preprocessing time series with aeon

TSFresh transform

Feature extraction with tsfresh transformer

Catch22 transform

The Canonical Time-series Characteristics (catch22) transform

Rocket transform

The ROCKET transform

MiniRocket transform

MiniRocket

SAST transform

Demo of SAST transform

Signature method

The Signature Method with aeon

Channel selection

1 Channel Selection in a Pipeline

Segmentation

Intro to segmentation

Time Series Segmentation

ClaSP segmentation

Time Series Segmentation with aeon and ClaSP

Hidalgo segmentation

Hidalgo segmentation

Distances

Distance functions

Distance Functions in aeon

Using aeon distances with scikit-learn

Using aeon distances with scikit-learn

Forecasting

Forecasting

Time series forecasting with aeon

Data Formatting and Loading

Data in aeon

Storing and Loading data in aeon

How series are stored in file and loaded into memory

Loading data into aeon

Example data sets

Provided datasets

Loading data from the web

Downloading and loading benchmarking datasets

Unequal length series

Loading data with unequal length series or missing values

Benchmarking

Benchmarking algorithms

Benchmarking - comparing estimator performance

Loading published results

Benchmarking: retrieving and comparing against published results

Getting estimator reference results

Benchmarking: retrieving and comparing against reference results

Benchmarking extrinsic regression algorithms

Benchmarking time series regression models

Base

aeon base classes

Overview of the base class structure

Series base classes

BaseSeriesEstimator

Visualisation

Plotting time series

Plotting Time Series

Plotting results

Results Plotting

Plotting for specific learning tasks

Plotting Time Series