growingWindowTest: Obtain the predictions of a model using a growing window...

In DMwR: Functions and data for "Data Mining with R"

Description

This function implements the growing window learning method that is frequently used in time series forecasting. The function allows applying this methodology to any modelling technique. The function returns the predictions of this technique, when applied using a growing window approach, for the given test set.

Usage

growingWindowTest(learner, form, train, test, relearn.step = 1, verbose = T)

Arguments

learner	This is an object of the class learner (type "class?learner" for details) representing the system to evaluate.
form	A formula describing the prediction problem.
train	A data frame with the initial training data. The size of this training set will also determine the size of the sliding window.
test	A data frame with the test set for which we want predictions.
relearn.step	A number indicating the number of test cases until a new model is re-learned by sliding the training window to cases that are nearest to the current test case.
verbose	A boolean determining the level of verbosity of the function.

Details

The growing window is a method frequently used to handle time series prediction problems. The basic motivation is that as time goes by the data gets "old" and thus the models should be re-learned to re-adjust for "fresher" data. This function implements this general idea for any modelling technique.

The function receives an initial training set. Using this initial set a first model is obtained with the supplied modelling technique. This model is applied to obtain predictions for the first relearn.step test cases. Afterwards a new model is obtained by adding the more recent training cases to the previous training set. This new training set will have a larger size than the initially provided training set. It will consist of the initial training set, plus the following relearn.step observations. This second model is again used to obtain predictions for another set of relearn.step test cases. The growing process keeps going until we obtain predictions for all provided test cases.

Value

A vector with the predictions for the test set. Note that if the target variable is a factor this vector will also be a factor.

Author(s)

Luis Torgo [e-mail]

References

Torgo, L. (2010) Data Mining using R: learning with case studies, CRC Press (ISBN: 9781439810187).

http://www.liaad.up.pt/~ltorgo/DataMiningWithR

See Also

slidingWindowTest,monteCarlo

Examples

data(swiss)

## Obtain the predictions of model rpartXse() for the last 22 rows of
## the swiss data set, when used with a growing window of 25 cases with
## a relearning step of 3

## The base learner used in the experiment
learnAndTest.rpartXse <- function(form, train, test, ...) {
    model <- rpartXse(form, train, ...)
    predict(model, test)
}

preds <- growingWindowTest(learner('learnAndTest.rpartXse',pars=list(se=0.5)),
                           Infant.Mortality ~ .,
                           swiss[1:25,],
                           swiss[26:nrow(swiss),],
                           3)

## Some statistics of these predictions
regr.eval(swiss[26:nrow(swiss),'Infant.Mortality'],preds,stats = c("mae", "mse", "rmse"))

Example output

Loading required package: lattice
Loading required package: grid
********
     mae      mse     rmse 
2.553779 9.942623 3.153193 

DMwR documentation built on May 1, 2019, 9:17 p.m.