Forecasting using SMI models — forecast.smimodel • smimodel

Returns forecasts and other information for SMI models.

Usage

# S3 method for class 'smimodel'
forecast(
  object,
  h = 1,
  level = c(80, 95),
  newdata,
  exclude.trunc = NULL,
  recursive = FALSE,
  recursive_colRange = NULL,
  ...
)

Arguments

object: An object of class smimodel. Usually the result of a call to model_smimodel.
h: Forecast horizon.
level: Confidence level for prediction intervals.
newdata: The set of new data on for which the forecasts are required (i.e. test set; should be a tsibble).
exclude.trunc: The names of the predictor variables that should not be truncated for stable predictions as a character string.
recursive: Whether to obtain recursive forecasts or not (default - FALSE).
recursive_colRange: If recursive = TRUE, the range of column numbers in newdata to be filled with forecasts.
...: Other arguments not currently used.

Value

An object of class forecast. Here, it is a list containing the following elements:

method: The name of the forecasting method as a character string.
model: The fitted model.
mean: Point forecasts as a time series.
residuals: Residuals from the fitted model.
fitted: Fitted values (one-step forecasts).

Examples

if(requireNamespace("gurobi", quietly = TRUE)){
  library(dplyr)
  library(ROI)
  library(tibble)
  library(tidyr)
  library(tsibble)

  # Simulate data
  n = 1015
  set.seed(123)
  sim_data <- tibble(x_lag_000 = runif(n)) |>
    mutate(
      # Add x_lags
      x_lag = lag_matrix(x_lag_000, 5)) |>
    unpack(x_lag, names_sep = "_") |>
    mutate(
      # Response variable
      y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
      # Add an index to the data set
      inddd = seq(1, n)) |>
    drop_na() |>
    select(inddd, y, starts_with("x_lag")) |>
    # Make the data set a `tsibble`
    as_tsibble(index = inddd)
  
  # Training set
  sim_train <- sim_data[1:1000, ]
  # Test set
  sim_test <- sim_data[1001:1010, ]

  # Index variables
  index.vars <- colnames(sim_data)[3:8]

  # Model fitting
  smimodel_ppr <- model_smimodel(data = sim_train,
                                yvar = "y",
                                index.vars = index.vars,
                                initialise = "ppr")

  forecast(smimodel_ppr, newdata = sim_test)
}