tsBoost: Boosting with Simple Linear Regression

In SLBDD: Statistical Learning for Big Dependent Data

Boosting with Simple Linear Regression

Description

It uses simple linear regression as the weak learner to perform L2 Boosting for time series data.

Usage

tsBoost(y, X, v = 0.01, m = 1000, rm.mean = TRUE)

Arguments

y	T by 1 scalar dependent variable.
X	T by k data matrix of predictors: T data points in rows with each row being data at a given time point, and k time series in columns.
v	Learning rate of boosting. Default value is 0.01.
m	Maximum number of boosting iterations. Default is 1000.
rm.mean	a logical command. Default is TRUE. If rm.mean=TRUE, both the dependent and predictors are mean-adjusted. If rm.mean=FALSE, no mean adjustment is made.

Value

A list containing:

• beta - the estimates of coefficient vector.

• residuals - residuals after the boosting fit.

• m - the maximum number of boosting iterations (from input).

• v - learning rate (from input).

• selection - the indexes for selected predictors. That is, the indexes for large beta estimates.

• count: the number of selected predictors.

• yhat - the fitted value of y.

Examples

data(TaiwanAirBox032017)
output <- tsBoost(TaiwanAirBox032017[,1], TaiwanAirBox032017[,2])

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