City Research Online

Abstract

This paper presents a visual analytics workflow for detecting stable cross-impact patterns in time series pairs. A sliding window technique computes multiple impact measures, including a novel Kendall's tau variant that tolerates minor fluctuations. Evaluating these measures across various time lags reveals dynamic relationships between time series. An interactive Ikat plot facilitates the exploration of impact distributions, helping identify intervals where specific cross-impacts remain stable (e.g., trends in one series followed by similar or opposite trends in another after a lag). These intervals are extracted as events, whose temporal (and, when applicable, spatial) distributions can be analyzed to uncover broader patterns across multiple time series pairs and over extended time spans. This includes identifying co-occurring cross-impacts and variations in cross-impact presence or type across different periods and data subsets. Experiments on real-world datasets demonstrate the framework's ability to isolate robust patterns, providing a scalable and interpretable approach to analyzing complex temporal dynamics.

Publication Type:	Article
Additional Information:	© 2026 IEEE. This accepted manuscript is made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Time series analysis, Market research, Data visualization,Fluctuations, Time measurement, Feature extraction, Forecasting, Anomaly detection, Synchronization, Shape
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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