evaluate.tspred {TSPred} | R Documentation |
Evaluate method for tspred
objects
Description
Evaluates the modeling fitness and quality of time series prediction of the trained models and
predicted time series data contained in a tspred
class object, respectively,
based on a particular metric. Each metric is defined
by an evaluating
object in the list contained in the tspred
class object.
Usage
## S3 method for class 'tspred'
evaluate(obj, fitness = TRUE, ...)
Arguments
obj |
An object of class |
fitness |
Should the function compute fitness quality metrics? |
... |
Other parameters passed to the method |
Details
The function evaluate.tspred
calls the method evaluate
on each evaluating
object contained in obj
. It uses each trained model,
the testing set and the time series predictions contained in obj
to compute the metrics.
Finally, the produced quality metrics are introduced in the structure of the tspred
class object in obj
.
Value
An object of class tspred
with updated structure containing
computed quality metric values.
Author(s)
Rebecca Pontes Salles
See Also
[tspred()] for defining a particular time series prediction process, and [MSE_eval()] for defining a time series prediction/modeling quality metric.
Other evaluate:
evaluate()
Examples
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)
tspred_1 <- evaluate(tspred_1)