Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting
  Approach
Rosana C. B. Rego
A proper forecast of the menstrual cycle is meaningful for women's health, as
it allows individuals to take preventive actions to minimize cycle-associated
discomforts. In addition, precise prediction can be useful for planning
important events in a woman's life, such as family planning. In this work, we
explored the use of machine learning techniques to predict regular and
irregular menstrual cycles. We implemented some time series forecasting
algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber
Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term
Memory Network. Moreover, we generated synthetic data to achieve our purposes.
The results showed that it is possible to accurately predict the onset and
duration of menstrual cycles using machine learning techniques.