select_model: Select the optimal model
In placeboo/amplify: Forecasting Daily Time Seriers with Double Seasonal Patterns

Description Usage Arguments Value Examples

double seasonal exponential smoothing method is implemented. If grid.search = TRUE, grid search is applied by searching parameters around the "first attempt" parameters. search.length and length.out are for grid search. The search is based on the minimum mean absolute percentage error with parallel computing. More details about parallel computing can be found in doParallel.

select_model(
  train.y,
  valid.y = NULL,
  s1 = 7,
  s2 = 7 * 52,
  h = 2 * max(period1, period2),
  grid.search = FALSE,
  search.length = c(-0.5, 0.5),
  length.out = 5,
  CV = FALSE,
  kfold = NULL
)

`train.y`	A numeric vector for training.
`valid.y`	A numeric vector for testing. If `grid.search = NULL`, `valid.y = NULL`.
`s1`	Period of the shorter seasonal period.
`s2`	Period of the longer seasonal period.
`h`	Number of periods for forecasting.
`grid.search`	If TRUE, a grid search is applied.
`search.length`	Grid search parameter. Only used if `grid.search = TRUE`. It is the proportion decreasing/increasing of the first attempt parameters. For example `search.length = c(-0.5, 0.5)`, if the first attempt parameter is 0.1, then the searching window is from `0.1 * (1 - 0.5)` to `0.1 * (1+ 0.5)`
`length.out`	Grid search parameter. Only used if `grid.search = TRUE`. It is the desired length of search sequence.
`CV`	If TRUE, k-fold cross validation applied
`kfold`	The number of K-fold

If grid.search = FALSE, an object of class forecast is return. Otherwise, a list contains:

model. An object of class forecast. The model is built by combining training and testing;
cv. A data frame of searched parameters with respect to its distance measures (see measure_dist)

data(tickets)
data.ls = train_test_split(tickets, var = "date", train.window = c(20140701, 20180630), test.window = c(20180701, 20190630))
train.y = data.ls$train.dat[,2]
valid.y = data.ls$test.dat[,2]
select.ls = select_model(train.y, valid.y, grid.search = TRUE, length.out = 2)
Double seasonal exponential time series cross validation