Time Series Forecasting Approach for Predictive Modeling of Next Menstrual Cycle Length
Paulina Julia Oliveira,
Caio Cavalcante,
Rosana Rego
Abstract: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 Ave… Show more
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