Second Faculty Mentor
When analyzing time series data, it is often of interest to categorize them based on how different they are. We define a new dissimilarity measure between time series: Dynamic Ordered Persistence Editing (DOPE). DOPE satisfies metric properties, is stable to noise, is as informative as alternative approaches, and efficiently computable. Satisfying these properties simultaneously makes DOPE of interest to both theoreticians and data scientists alike.
Arbelo, Jose; Delgado, Antonio; Kirk, Charley; and Schlamowitz, Zach, "The DOPE Distance is SIC: A Stable, Informative, and Computable Metric on Ordered Merge Trees" (2022). Mathematics Summer Fellows. 15.
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