Description Usage Arguments Details Value Author(s) See Also
This function is almost identical to the maeforecast
function excpet that it only returns the out-of-sample point forecasts. It is designed primarily for the use of the Bagging
and bt.interval
functions.
1 2 | maeforecast.simplified(data, model="ar", w_size, window="recursive",
y.index=1, h=0, ...)
|
data |
a data frame or a matrix; the first column should contain the time series variable for which the forecasts are to be made. Other columns should contain the covariates. |
model |
character, indicating which model should be used to make the forecasts. Default is an AR(1) model. Note that |
w_size |
numeric, indicating the index where the forecasting should begin. If the first point forecast should be made at the 73th observation, for example, |
window |
character, indicating the forecasting scheme to be applied. Options include |
y.index |
numeric, indicating the column position of the time series for which the forecasts are made (Y). Defualt is |
h |
forecasting horizon. Default is |
... |
other arguments that may be used. Refer to |
Supported models include "ar"
, "rw"
(Random Walk), "lasso"
, "postlasso"
(Post-Lasso), "ridge"
, "alasso"
(Adaptive Lasso), "postalasso"
(Post-AdaptiveLasso), "postnet"
(Post-Adaptive ElasticNet), "rf"
(Random Forests), "dfm"
& "dfm2"
(Dynamic Factor Models).
A vector of out-of-sample point forecasts.
Zehua Wu
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