sliding_ase_var: Function to calculate the sliding window ASE for a model...

In josephsdavid/tswgewrapped: Helpful wrappers for 'tswge', 'vars' and 'nnfor' time series packages

Description

Function to calculate the sliding window ASE for a model Supports VAR Model from the vars package

Usage

sliding_ase_var(
  data,
  var_interest,
  k,
  trend_type = NA,
  season = NULL,
  n.ahead = NA,
  batch_size = NA,
  step_n.ahead = TRUE,
  verbose = 0,
  ...
)

Arguments

data	The dataframe containing the time series realizations (data should not contain time index)
var_interest	The output variable of interest (dependent variable)
k	The lag value to use for the VAR model (generally determined by the VARselect function)
trend_type	The trend type to use in VARselect and the VAR model. Refer to vars::VARselect and vars::VAR for valid options.
season	The seasonality to use in the VAR model.
n.ahead	last n.ahead data points in each batch will be used for prediction and ASE calculations
batch_size	Window Size used
step_n.ahead	Whether to step each batch by n.ahead values (Default = FALSE)
verbose	How much to print during the model building and other processes (Default = 0)
...	Additional arguments to pass to the VAR model

Value

Named list 'ASEs' - ASE values 'time_test_start' - Time Index indicating start of test time corresponding to the ASE values 'time_test_end' - Time Index indicating end of test time corresponding to the ASE values 'batch_num' - Indicates the batch number for each ASE value 'AICs' = The AIC values for the individual batches 'BICs' = The BIC values for the individual batches 'f' - Forecasts for each batch 'll' - Lower Forecast Limit for each batch 'ul' - Upper Forecast Limit for each batch 'time.forecasts' - Time Corresponding to each forecast, upper and lower limit values

josephsdavid/tswgewrapped documentation built on July 31, 2020, 9:36 a.m.