This website contains supporting materials for our paper "ClaSP - Time Series Segmentation" published at CIKM'21. If you want to use ClaSP in your scientific publication or application, please use the updated and maintained claspy Python package.
Source Codes
ClaSP Code: A Python3 implementation of our code and all 98 datasets
FLOSS Code: A Python3 implementation of FLOSS
Autoplait Code: A C implementation of Autoplait
Ruptures Code: A Python3 implementation of BinSeg-L2 / Window-L2
BOCD: A Python3 implementation of BOCD
The 98 Benchmark datasets
We wish to thank the data donors and archivists of the two benchmark sets for segmentation [1] and the UCR classification archive [2].
FLOSS Benchmark Datasets: 32 segmentation datasets (see [1]) used for the experiments
Semi-synthetic Benchmark Generator: A Jupyter Notebook to generate 66 semi-synthetic segmentation datasets from the UCR classification archive (see [2])
Dataset Description (Raw CSV, Raw Images): An overview of all 98 datasets
Results presented in the paper
Design Choices (Raw CSV): The errors for all tested parameter combinations
Segmentation (Raw CSV): The errors for ClaSP and its competitors
Runtime (Raw CSV): The runtimes for ClaSP and its competitors
Window Size Selection (Raw CSV): The errors for all tested window size selection strategies
Segmentation Visualizations (Raw Images): ClaSP segmentations for all 98 time series
Complex Time Series Classifiers (Raw CSV): The errors for ClaSP with different classifiers
References
[1] Supporting Website for "Domain Agnostic Online Semantic Segmentation": https://www.cs.ucr.edu/~eamonn/FLOSS/
[2] UEA & UCR Time Series classification archive: http://www.timeseriesclassification.com