t_oos_mc: Time-wise Monte Carlo

In mrfoliveira/STEvaluation-MDPI2021: Evaluation procedures for forecasting with spatio-temporal data

Description

Performs a time-wise Monte Carlo experiment where split points are randomly chosen and a window of previous observations are used for training, with a window of following observations used for testing.

Usage

t_oos_mc(
  data,
  tr.perc,
  ts.perc,
  nreps,
  FUN,
  form,
  time = "time",
  site_id = "site",
  .keepTrain = TRUE,
  .parallel = FALSE,
  .verbose = ifelse(.parallel, FALSE, TRUE),
  ...
)

Arguments

data	full dataset
tr.perc	percentage of data used for training. Remaining will be used for testing
ts.perc	percentage of data used for testing
nreps	number of repetitions/split-points in experiment
FUN	function with arguments train training set test testing set time column name of time-stamps site_id column name of location identifiers form a formula for model learning ... other arguments
form	a formula for model learning
time	column name of time-stamp in data. Default is "time"
site_id	column name of location identifier in data. Default is "site_id"
.keepTrain	if TRUE (default), instead of the results of FUN being directly returned, a list is created with both the results and a data.frame with the time and site identifiers of the observations used in the training step.
.parallel	Boolean indicating whether each fold should be run in parallel
.verbose	if TRUE, prints information about progress of the experiment. Default FALSE unless experiment is run in parallel
...	other arguments to FUN

Value

If keepTrain is TRUE, a list where each slot corresponds to one repetition or fold, containing a list with slots results containing the results of FUN, and train containing a data.frame with the time and site_id identifiers of the observations used in the training step. Usually, the results of FUN is a data.frame with location identifier site_id, time-stamp time, true values trues and the workflow's predictions preds.

mrfoliveira/STEvaluation-MDPI2021 documentation built on Dec. 21, 2021, 10:07 p.m.