O'Reilly logo

Practical Time Series Analysis by Dr. PKS Prakash, Dr. Avishek Pal

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Multi-layer perceptrons

Multi-layer perceptrons (MLP) are the most basic forms of neural networks. An MLP consists of three components: an input layer, a bunch of hidden layers, and an output layer. An input layer represents a vector of regressors or input features, for example, observations from preceding p points in time [xt-1,xt-2, ... ,xt-p]. The input features are fed to a hidden layer that has n neurons, each of which applies a linear transformation and a nonlinear activation to the input features. The output of a neuron is gi = h(wix + bi), where wi and bi are the weights and bias of the linear transformation and h is a nonlinear activation function. The nonlinear activation function enables the neural network to model complex non-linearities ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required