Model-free prediction of noisy chaotic time series by deep learning

Understand

We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations.

• The proposed deep learning model aims to predict the conditional probability distribution of a state variable.
• The Long Short-Term Memory network (LSTM) is employed to model the nonlinear dynamics and a softmax layer is used to approximate a probability distribution.
• The LSTM model is trained by minimizing a regularized cross-entropy function.