Forecast a model from the fable intermittentATA model

# S3 method for intermittentATA
forecast(
  object,
  new_data,
  h = NULL,
  ci_level = 95,
  negative_forecast = TRUE,
  onestep = FALSE,
  ...
)

Arguments

object: The time series model used to produce the forecasts
new_data: A `tsibble` containing future information used to forecast.
h: The forecast horison (can be used instead of `new_data` for regular time series with no exogenous regressors).
ci_level: Confidence Interval levels for forecasting. Default value is 95.
negative_forecast: Negative values are allowed for forecasting. Default value is TRUE. If FALSE, all negative values for forecasting are set to 0.
onestep: Default is FALSE. if TRUE, the dynamic forecast strategy uses a one-step model multiple times `h` forecast horizon) where the prediction for the prior time step is used as an input for making a prediction on the following time step.
...: Other arguments

Value

A vector of fitted residuals.

Examples

library(intermittentATA)
as_tsibble(fmcgData) %>%
   model(crostonata = intermittentATA(value ~ d_trend(type = "M", parQ = 1) + i_trend("A") + intermittent("croston"))) %>% forecast(h=6)
#> # A fable: 6 x 4 [1M]
#> # Key:     .model [1]
#>   .model        index     value .mean
#>   <chr>         <mth>    <dist> <dbl>
#> 1 crostonata 2019 Jan 0.1693098 0.169
#> 2 crostonata 2019 Feb 0.1693098 0.169
#> 3 crostonata 2019 Mar 0.1693098 0.169
#> 4 crostonata 2019 Apr 0.1693098 0.169
#> 5 crostonata 2019 May 0.1693098 0.169
#> 6 crostonata 2019 Jun 0.1693098 0.169