SLBDD (version 0.0.4)

tsBoost: 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

# NOT RUN {
data(TaiwanAirBox032017)
output <- tsBoost(TaiwanAirBox032017[,1], TaiwanAirBox032017[,2])
# }