Description Usage Arguments Details Value Author(s) See Also Examples
benchmark
is a generic function for benchmarking results based on particular metrics.
The function invokes particular methods which
depend on the class of the first argument.
1 2 3 4 |
obj |
An object of class |
... |
Ignored |
bmrk_objs |
A list of objects of class |
rank.by |
A vector of the given names of the metrics that should base the ranking. |
The function benchmark.tspred
benchmarks a time series prediction process
defined by a tspred
object based on a particular metric. The metrics resulting
from its execution are compared against the ones produced by other time series prediction
processes (defined in a list of tspred
objects).
A list containing:
rank |
A data.frame with the ranking of metrics computed for the benchmarked |
ranked_tspred_objs |
A list of the benchmarked |
Rebecca Pontes Salles
[tspred()] for defining a particular time series prediction process.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | #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()
eval2 <- MAPE_eval()
#Defining a time series prediction process
tspred_1 <- tspred(subsetting=proc1,
processing=list(BCT=proc2,
WT=proc3),
modeling=modl1,
evaluating=list(MSE=eval1,
MAPE=eval2)
)
summary(tspred_1)
#Obtaining objects of the processing class
proc4 <- SW(window_len = 6)
proc5 <- MinMax()
#Obtaining objects of the modeling class
modl2 <- NNET(size=5,sw=proc4,proc=list(MM=proc5))
#Defining a time series prediction process
tspred_2 <- tspred(subsetting=proc1,
processing=list(BCT=proc2,
WT=proc3),
modeling=modl2,
evaluating=list(MSE=eval1,
MAPE=eval2)
)
summary(tspred_2)
data("CATS")
data <- CATS[3]
tspred_1_run <- workflow(tspred_1,data=data,prep_test=TRUE,onestep=TRUE)
tspred_2_run <- workflow(tspred_2,data=data,prep_test=TRUE,onestep=TRUE)
b <- benchmark(tspred_1_run,list(tspred_2_run),rank.by=c("MSE"))
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