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TimeSeriesClustering: An extensible framework in Julia

Teichgraeber, Holger; Kuepper, Lucas Elias; Brandt, Adam R.

TimeSeriesClustering is a Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.
The software provides a type system for temporal data, and provides an implementation of the most commonly used clustering methods and extreme value selection methods for temporal data.
TimeSeriesClustering provides simple integration of multi-dimensional time-series data (e.g., multiple attributes such as wind availability, solar availability, and electricity demand) in a single aggregation process.
The software is applicable to general time series datasets and lends itself well to a multitude of application areas within the field of time series data mining.
TimeSeriesClustering was originally developed to perform time series aggregation for energy systems optimization problems.


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