Comparing Similarity Perception in Time Series Visualizations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Visualization and Computer Graphics Year : 2018

Comparing Similarity Perception in Time Series Visualizations

Abstract

A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.
Fichier principal
Vignette du fichier
infovis2018-author.pdf (1.82 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01845008 , version 1 (19-07-2018)
hal-01845008 , version 2 (08-11-2019)
hal-01845008 , version 3 (30-08-2020)

Identifiers

Cite

Anna Gogolou, Theophanis Tsandilas, Themis Palpanas, Anastasia Bezerianos. Comparing Similarity Perception in Time Series Visualizations. IEEE Transactions on Visualization and Computer Graphics, 2018, TVCG 2019 (InfoVis 2018), 25 (1), pp.523 - 533. ⟨10.1109/TVCG.2018.2865077⟩. ⟨hal-01845008v3⟩
951 View
5541 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More