Detecting Stable Cross-Impact Patterns in Bivariate Time Series

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.