sliding_ase_var.Rd
Function to calculate the sliding window ASE for a model Supports VAR Model from the vars package
sliding_ase_var( data, var_interest, k, trend_type = NA, season = NULL, n.ahead = NA, batch_size = NA, step_n.ahead = TRUE, verbose = 0, ... )
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 |
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