| tsBoost {SLBDD} | R Documentation |
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])