postprocess.tspred: Postprocess method for 'tspred' objects
In TSPred: Functions for Benchmarking Time Series Prediction

Description Usage Arguments Details Value Author(s) See Also Examples

Performs postprocessing of the predicted time series data contained in a tspred class object reversing a particular set of transformation methods. Each transformation method is defined by a processing object in the list contained in the tspred class object.

1 2	## S3 method for class 'tspred' postprocess(obj, ...)

`obj`	An object of class `tspred` defining a particular time series prediction process.
`...`	Other parameters passed to the method `postprocess` of the `processing` objects from `obj`.

The function postprocess.tspred recursively calls the method postprocess on each processing object contained in obj in the inverse order as done by preprocess.tspred. The postprocessed predictions resulting from each of these calls is used as input to the next call. Finally, the produced postprocessed time series predictions are introduced in the structure of the tspred class object in obj.

The same transformation method parameters used/computed during preprocessing, duly saved in the structure of the tspred class object in obj, are used for reversing the transformations during postprocessing.

An object of class tspred with updated structure containing postprocessed time series predictions.

Rebecca Pontes Salles

[tspred()] for defining a particular time series prediction process, and [LT()] for defining a time series transformation method.

Other preprocess: preprocess.tspred(), subset()

data(CATS)

#Obtaining objects of the processing class
proc1 <- subsetting(test_len=20)
proc2 <- BoxCoxT(lambda=NULL)
proc3 <- WT(level=1, filter="bl14")

#Obtaining objects of the modeling class
modl1 <- ARIMA()

#Obtaining objects of the evaluating class
eval1 <- MSE_eval()

#Defining a time series prediction process
tspred_1 <- tspred(subsetting=proc1,
                   processing=list(BCT=proc2,
                                   WT=proc3),
                   modeling=modl1,
                   evaluating=list(MSE=eval1)
)
summary(tspred_1)

tspred_1 <- subset(tspred_1, data=CATS[3])
tspred_1 <- preprocess(tspred_1,prep_test=FALSE)
tspred_1 <- train(tspred_1)
tspred_1 <- predict(tspred_1, onestep=TRUE)
tspred_1 <- postprocess(tspred_1)