Stoppacher, StefanieRakuschek, JulianSchreck, TobiasSchulz, Hans-JörgVillanova, Anna2025-05-262025-05-262025978-3-03868-283-72664-4487https://doi.org/10.2312/eurova.20251097https://diglib.eg.org/handle/10.2312/eurova20251097Seasonal variations in energy consumption and temperature, like many other time series, exhibit periodically repeating patterns. Identifying and analyzing these cyclic patterns is crucial for understanding underlying trends and predicting future behavior. Spiral visualizations are commonly used to highlight periodicity, as they intuitively arrange seasonal data in spirals. We introduce encompassing user-guided enhancements to spiral visualizations, supporting the search and analysis of patterns in cyclic time series. A key element is a parameter space visualization by an interactive heat map, which highlights important quality measures, such as similarity and monotonicity, across different segments of the spiral. This approach helps users efficiently locate areas of interest that meet specific criteria, thereby streamlining the discovery of significant patterns. To further support analysis, the system offers a linked stacked area or line chart representation of selected segments, providing a clearer understanding of the quality measures. The effectiveness of the quality measures is demonstrated by use cases on several datasets.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual analyticsHuman centered computing → Visual analytics10.2312/eurova.202510976 pages