# Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

arXiv: https://arxiv.org/abs/2308.07927 · HTML: https://mlsanity.com/paper/2308.07927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 11 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Modeling Individual Cyclic Variation in Human Behavior](https://mlsanity.com/paper/1712.05748/) (2017)
- [Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit](https://mlsanity.com/paper/1806.00650/) (2018)
- [A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data](https://mlsanity.com/paper/2102.12439/) (2021)

## Abstract (arXiv)

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.