10.5281/zenodo.3385349
https://zenodo.org/records/3385349
oai:zenodo.org:3385349
Teichgraeber, Holger
Holger
Teichgraeber
0000-0002-4061-2226
Stanford University
Kuepper, Lucas Elias
Lucas Elias
Kuepper
0000-0002-1992-310X
Stanford University
Brandt, Adam R.
Adam R.
Brandt
0000-0002-2528-1473
Stanford University
TimeSeriesClustering: An extensible framework in Julia
Zenodo
2019
Unsupervised Learning
Clustering
Time series
Julia
Machine learning
Energy Systems Optimization
Representative periods
Typical days
2019-09-04
10.5281/zenodo.3385348
0.5.2
MIT License
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.
The software can be found at: https://github.com/holgerteichgraeber/TimeSeriesClustering.jl
https://github.com/holgerteichgraeber/TimeSeriesClustering.jl