imputation (version 2.0.3)

cv.tsImpute: CV for tsImpute

Description

Cross Validation for Time Series Imputation Artificially erase some data and run gbmImpute. Compute the RMSE on the subset of x for which data was artificially erased.

Usage

cv.tsImpute(time, dimension, metric, ...)

Arguments

time

a vector of dates or datetime objects

dimension

a data frame of exogenous predictor variables

metric

a matrix where each column represents a time series

...

extra parameters to be passed to tsImpute

Examples

dates = timeSequence(from = '2012-01-01', to = '2012-12-31', by = 'day')
  dimensions = sample(c("A", "B"), 366, replace = TRUE)
  numA = length(which(dimensions == "A")); numB = length(which(dimensions == "B"))
  metrics = matrix(0, 366, 2)
  metrics[which(dimensions == "A"),1] = rnorm(numA, mean=1)
  metrics[which(dimensions == "A"),2] = rnorm(numA, mean=5)
  metrics[which(dimensions == "B"),1] = rnorm(numB, mean=-10)
  metrics[which(dimensions == "B"),2] = rnorm(numB, mean=-5)
  tp = projectDate(as.Date(dates))
  monday.indices = which(tp$weekday == "Monday")
  metrics[sample(monday.indices, 20),] = NA
  cv.tsImpute(as.Date(dates), dimensions, metrics)