月经周期长度的预测建模：一种时间序列预测方法
Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach
AI总结：
该研究面向规律与不规律月经周期预测，比较多种时间序列机器学习算法并使用合成数据，验证了准确预测周期开始时间与持续时间的可行性。
摘要：
对月经周期进行恰当预测对女性健康具有重要意义，因为它能让个人采取预防性措施，最大限度减少与周期相关的不适。此外，精确预测还有助于规划女性生活中的重要事件，例如计划生育。在本研究中，我们探索了使用机器学习技术来预测规律和不规律的月经周期。我们实现了多种时间序列预测算法方法，例如自回归积分滑动平均（AutoRegressive Integrated Moving Average）、Huber回归、Lasso回归、正交匹配追踪（Orthogonal Matching Pursuit）以及长短期记忆网络（Long Short-Term Memory Network）。此外，我们生成了合成数据以实现研究目标。结果表明，使用机器学习技术准确预测月经周期的开始时间和持续时间是可能的。
英文摘要：
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.
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