Python for Finance Cookbook

Multilayer perceptrons for time series forecasting

Multilayer perceptrons (MLP) are one of the basic architectures of neural networks. At a very high level, they consist of three components:

• The input layer: A vector of features.
• The hidden layers: Each hidden layer consists of N neurons.
• The output layer: Output of the network; depends on the task (regression/classification).

The input of each hidden layer is first transformed linearly (multiplication by weights and adding the bias term) and then non-linearly (by applying activation functions such as ReLU). Thanks to the non-linear activation, the network is able to model complex, non-linear relationships between the features and the target.

A multilayer perceptron contains multiple hidden ...