Stochastic differential equations such as the Ornstein-Uhlenbeck process have long been used to model realworld probablistic events such as stock prices and temperature fluctuations. While statistical methods such as Maximum Likelihood Estimation (MLE), Kalman Filtering, Inverse Variable Method, and more have historically been used to estimate the parameters of stochastic differential equations, the recent explosion of deep learning technology suggests that models such as a Recurrent Neural Network (RNN) could produce more precise estimators. We present a series of experiments that compare the estimation accuracy and computational expensiveness of a statistical method (MLE) with a deep learning model (RNN) for the parameters of the Ornstein-Uhlenbeck process.

翻译：诸如Ornstein-Uhlenbeck过程之类的随机微分方程长期以来被用于模拟现实世界中的概率事件，例如股票价格和温度波动。虽然历史上使用最大似然估计（MLE）、卡尔曼滤波、逆变量法等统计方法来估计随机微分方程的参数，但深度学习技术的近期爆发式发展表明，诸如循环神经网络（RNN）之类的模型可能产生更精确的估计量。我们进行了一系列实验，针对Ornstein-Uhlenbeck过程的参数，比较了一种统计方法（MLE）与一种深度学习模型（RNN）的估计精度和计算开销。