Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and tedious to explore. Automated analysis methods exist, but can be too costly for smooth, interactive analysis. We propose a compact visual representation that reveals periodicity by showing a phase histogram for a given period length that can be used standalone or in combination with other linked visualizations. Our approach supports guided, interactive analyses by suggesting other period lengths to explore, which are ranked based on two quality measures. We further describe how the phase can be mapped to visual representations in other views to reveal periodicity there.
%0 Conference Paper
%1 franke2023periodicity
%A Franke, Max
%A Koch, Steffen
%B 2023 IEEE Visualization and Visual Analytics (VIS)
%D 2023
%I IEEE
%K myown vis(us) vis-gis visus:frankemx visus:kochsn
%P 191-195
%R 10.1109/VIS54172.2023.00047
%T Compact Phase Histograms for Guided Exploration of Periodicity
%X Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and tedious to explore. Automated analysis methods exist, but can be too costly for smooth, interactive analysis. We propose a compact visual representation that reveals periodicity by showing a phase histogram for a given period length that can be used standalone or in combination with other linked visualizations. Our approach supports guided, interactive analyses by suggesting other period lengths to explore, which are ranked based on two quality measures. We further describe how the phase can be mapped to visual representations in other views to reveal periodicity there.
@inproceedings{franke2023periodicity,
abstract = {Periodically occurring accumulations of events or measured values are present in many time-dependent datasets and can be of interest for analyses. The frequency of such periodic behavior is often not known in advance, making it difficult to detect and tedious to explore. Automated analysis methods exist, but can be too costly for smooth, interactive analysis. We propose a compact visual representation that reveals periodicity by showing a phase histogram for a given period length that can be used standalone or in combination with other linked visualizations. Our approach supports guided, interactive analyses by suggesting other period lengths to explore, which are ranked based on two quality measures. We further describe how the phase can be mapped to visual representations in other views to reveal periodicity there.},
added-at = {2024-04-04T16:58:28.000+0200},
author = {Franke, Max and Koch, Steffen},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c40208bb8a3c9d9e214f26fc8e8029c9/maxfranke},
booktitle = {2023 IEEE Visualization and Visual Analytics (VIS)},
doi = {10.1109/VIS54172.2023.00047},
interhash = {49a459efcea38607ceb0b886a0816710},
intrahash = {c40208bb8a3c9d9e214f26fc8e8029c9},
issn = {2771-9553},
keywords = {myown vis(us) vis-gis visus:frankemx visus:kochsn},
month = oct,
pages = {191-195},
preprinturl = {https://arxiv.org/abs/2307.15483},
publisher = {IEEE},
timestamp = {2024-04-04T16:58:28.000+0200},
title = {Compact Phase Histograms for Guided Exploration of Periodicity},
year = 2023
}
