Obtaining forecasts on a test set from a fitted smimodel

Gives forecasts on a test set.

Usage

# S3 method for class 'smimodel'
predict(
  object,
  newdata,
  exclude.trunc = NULL,
  recursive = FALSE,
  recursive_colRange = NULL,
  ...
)

Arguments

object: A smimodel object.
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. (Since the nonlinear functions are estimated using splines, extrapolation is not desirable. Hence, if any predictor variable in newdata that is treated non-linearly in the estimated model, will be truncated to be in the in-sample range before obtaining predictions. If any variables are listed here will be excluded from such truncation.)
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. Recursive/autoregressive forecasting is required when the lags of the response variable itself are used as predictor variables into the model. Make sure such lagged variables are positioned together in increasing lag order (i.e. lag_1, lag_2, ..., lag_m, lag_m = maximum lag used) in newdata, with no break in the lagged variable sequence even if some of the intermediate lags are not used as predictors.
...: Other arguments not currently used.

Value

A tsibble with forecasts on test set.

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")

  predict(object = smimodel_ppr, newdata = sim_test)
}