Estimating Parameters of the Ornstein-Uhlenbeck Process: A Comparison of Traditional and Deep Learning Methods

Deep learning methods, such as multi-layer perceptrons, can accurately estimate the parameters of the Ornstein-Uhlenbeck process given a large dataset of observed trajectories, but traditional methods like the Kalman filter and maximum likelihood estimation may be more suitable for smaller datasets.