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

TL;DR

This paper introduces a state-space model that uses basal body temperature time series to predict menstruation onset more accurately than traditional calendar methods, offering a new statistical approach for menstrual cycle forecasting.

Contribution

The paper presents a novel state-space modeling framework incorporating menstrual phase as a latent variable for improved BBT-based cycle prediction.

Findings

The proposed model outperformed calendar calculations in predicting menstruation onset.

Sequential Bayesian filtering effectively estimated menstrual phase from BBT data.

The framework can be extended to predict other menstrual cycle-related events.

Abstract

Women's basal body temperature (BBT) follows a periodic pattern that is associated with the events in their menstrual cycle. Although daily BBT time series contain potentially useful information for estimating the underlying menstrual phase and for predicting the length of current menstrual cycle, few models have been constructed for BBT time series. Here, we propose a state-space model that includes menstrual phase as a latent state variable to explain fluctuations in BBT and menstrual cycle length. Conditional distributions for the menstrual phase were obtained by using sequential Bayesian filtering techniques. A predictive distribution for the upcoming onset of menstruation was then derived based on the conditional distributions and the model, leading to a novel statistical framework that provided a sequentially updated prediction of the day of onset of menstruation. We applied this framework to a real dataset comprising women's self-reported BBT and days of menstruation, comparing the prediction accuracy of our proposed method with that of conventional calendar calculation. We found that our proposed method provided a better prediction of the day of onset of menstruation. Potential extensions of this framework may provide the basis of modeling and predicting other events that are associated with the menstrual cycle.