evaluate.tspred: Evaluate method for 'tspred' objects
In TSPred: Functions for Benchmarking Time Series Prediction

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

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.

1 2	## S3 method for class 'tspred' evaluate(obj, fitness = TRUE, ...)

`obj`	An object of class `tspred` defining a particular time series prediction process.
`fitness`	Should the function compute fitness quality metrics?
`...`	Other parameters passed to the method `evaluate` of the `evaluating` objects from `obj`.

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.

An object of class tspred with updated structure containing computed quality metric values.

Rebecca Pontes Salles

[tspred()] for defining a particular time series prediction process, and [MSE_eval()] for defining a time series prediction/modeling quality metric.

Other evaluate: evaluate()

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)