Before revision:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators  show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines  .
After revision:  Our experiments using real mHealth cycle length data from 5 ,000 menstruators show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines , while providing insights on disentangling menstrual patterns from self-tracking artifacts. This work can benefit users, mHealth app developers, and researchers in better understanding cycle patterns and user adherence .