Forecasting using nonparametric additive models with backward elimination

Returns forecasts and other information for nonparametric additive models with backward elimination.

Usage

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

Arguments

object: An object of class backward. Usually the result of a call to model_backward.
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

library(dplyr)
library(tibble)
library(tidyr)
library(tsibble)

# Simulate data
n = 1215
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, ]
# Validation set
sim_val <- sim_data[1001:1200, ]
# Test set
sim_test <- sim_data[1201:1210, ]

# Predictors taken as non-linear variables
s.vars <- colnames(sim_data)[3:8]

# Model fitting
backwardModel <- model_backward(data = sim_train,
                                val.data = sim_val,
                                yvar = "y",
                                s.vars = s.vars)
forecast(backwardModel, newdata = sim_test)
#>    Point Forecast
#> 1       2.8363732
#> 2       1.5316512
#> 3      -0.3403322
#> 4       2.1579572
#> 5       1.1067827
#> 6       0.4067579
#> 7       3.2047193
#> 8       1.5858674
#> 9       0.5086024
#> 10      0.9975114