rnnStream: Setup the Input and Output for a Recurrent Neural Network

In SLBDD: Statistical Learning for Big Dependent Data

Setup the Input and Output for a Recurrent Neural Network

Description

R command to setup the input and output for a Recurrent Neural Network. It is used in the Wiley book Statistical Learning with Big Dependent Data by Daniel Peña and Ruey S. Tsay (2021).

Usage

rnnStream(z, h = 25, nfore = 200)

Arguments

z	Input in integer values.
h	Number of lags used as input.
nfore	Data points in the testing subsample.

Value

A list containing:

• Xfit - Predictor in training sample (binary).

• Yfit - Dependent variable in the training sample (binary).

• yp - Dependent variable in testing sample.

• Xp - Predictor in the testing sample (binary).

• X - Predictor in the training sample.

• yfit - Dependent variable in the training sample.

• newX - Predictor in the testing sample.

Examples

output <- rnnStream(rnorm(100), h=5, nfore=20)

SLBDD documentation built on April 27, 2022, 5:08 p.m.