First Multivariate Time Series Archive from UEA (2018)
This new release is a simple collection of many related signals used to teach computers how to tell different patterns apart.
The goal was to make tests fairer, so everyone can try the same data and see what really works.
The set focuses on multivariate cases — that means many measurements per example — and is the first big public archive for this kind of work.
The 2018 edition brings 30 datasets that cover a wide mix of problems and sizes, and all files were made standardized so they are easier to compare.
There are no missing values, every series is the same length, and ready-made train and test splits are included.
This helps makers and students test ideas on the same ground, so claims become clearer.
Try it if you want simple, shared data to compare methods — it saves time and makes research more honest, and many groups will benefit, even if they are new to this field.
Read article comprehensive review in Paperium.net:
The UEA multivariate time series classification archive, 2018
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