The forecasting of menstruation based on a state-space modeling of basal body temperature time series

Keiichi Fukaya, Ai Kawamori, Yutaka Osada, Masumi Kitazawa, Makio Ishiguro

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that involves the menstrual phase as a latent state variable to explain the daily fluctuation of BBT and the menstruation cycle length. Conditional distributions of the phase are obtained by using sequential Bayesian filtering techniques. A predictive distribution of the next menstruation day can be derived based on this conditional distribution and the model, leading to a novel statistical framework that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed.

Original languageEnglish
Pages (from-to)3361-3379
Number of pages19
JournalStatistics in Medicine
Issue number21
Publication statusPublished - 2017 Sept 20


  • basal body temperature
  • menstrual cycle length
  • periodic phenomena
  • sequential prediction
  • state-space model


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