A Comparison of Traditional and Deep Learning Methods for Parameter
  Estimation of the Ornstein-Uhlenbeck Process
Jacob Fein-Ashley
We consider the Ornstein-Uhlenbeck (OU) process, a stochastic process widely
used in finance, physics, and biology. Parameter estimation of the OU process
is a challenging problem. Thus, we review traditional tracking methods and
compare them with novel applications of deep learning to estimate the
parameters of the OU process. We use a multi-layer perceptron to estimate the
parameters of the OU process and compare its performance with traditional
parameter estimation methods, such as the Kalman filter and maximum likelihood
estimation. We find that the multi-layer perceptron can accurately estimate the
parameters of the OU process given a large dataset of observed trajectories;
however, traditional parameter estimation methods may be more suitable for
smaller datasets.