DLdata: Create an input data matrix for a Deep learning program that...

In SLBDD: Statistical Learning for Big Dependent Data

Create an input data matrix for a Deep learning program that uses time series data.

Description

R command to setup the training and forecasting data for deep learning.

Usage

DLdata(x, forerate = 0.2, locY = 1, lag = 1)

Arguments

x	T by k data matrix: T data points in rows and k time series in columns.
forerate	Fraction of sample size to form the forecasting (or testing) sample.
locY	Locator for the dependent variable.
lag	Number of lags to be used to form predictors.

Value

A list containing:

• Xtrain - Standardized predictors matrix.

• Ytrain - Dependent variable in training sample.

• Xtest - Predictor in testing sample, standardized according to X_train.

• Ytest - Dependent variable in the testing sample.

• nfore - Number of forecasts.

Examples

x <- matrix(rnorm(7000), nrow=700, ncol=100)
m1 <- DLdata(x, forerate=c(200/nrow(x)), lag=6, locY=6)

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