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

rnnStreamR Documentation

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.

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