Loading/storing/converting data in aeon#

Getting data into the correct data structure is fundamental. These notebooks cover:

Data structures and containers for aeon estimators

aeon includes algorithms for time series forecasting and machine learning. These two communities have different conventions on how to store data and what to call data structures. The data structures and containers notebook goes into more detail as to how we store data, with recommendations on what to use for different learning tasks.

Converting from one data structure to another

aeon provides tools from converting from one data structure to another. Estimators internally handle conversion to the one they need internally. The data conversions notebook provides an overview of these conversions.

Loading data from file

aeon provides functions to load data directly from text files in several formats. The data loading notebook describes the formats of our supported files and how to load them into aeon data structures.

Provided datasets

aeon ships with a range of datasets used in examples and testing. The provided datasets notebook describes all these datasets.

Loading data from online repositories.

You can load data directly from our sister websites Time Series Machine Learning archive and the [Monash time series forecasting] ( sites. More details in the load from web notebook

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Generated using nbsphinx. The Jupyter notebook can be found here.