Exploring and visualizing temporal relations in multivariate time series

This paper introduces an approach to analysing multivariate time series (MVTS) data through progressive temporal abstraction of the data into patterns characterizing behavior of the studied dynamic phenomenon. The paper focuses on two core challenges: identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior. The proposed approach combines existing methods for univariate pattern extraction, computation of temporal relations according to the Allen’s time interval algebra, visual displays of the temporal relations, and interactive query operations into a cohesive visual analytics workflow. The paper describes application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match, illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

  • Published in:
    Visual Informatics
  • Type:
    Article
  • Authors:
    Shirato, Gota; Andrienko, Natalia; Andrienko, Gennady
  • Year:
    2023

Citation information

Shirato, Gota; Andrienko, Natalia; Andrienko, Gennady: Exploring and visualizing temporal relations in multivariate time series, Visual Informatics, 2023, https://www.sciencedirect.com/science/article/pii/S2468502X23000396, Shirato.etal.2023b,

Associated Lamarr Researchers

lamarr institute person Andriyenko Nathaliya pi - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Natalia Andrienko

Area Chair Human-centered AI Systems to the profile
lamarr institute person Andriyenko Gennadiy pi - Lamarr Institute for Machine Learning (ML) and Artificial Intelligence (AI)

Prof. Dr. Gennady Andrienko

Principal Investigator Human-centered AI Systems to the profile