forecast.gamFit: Forecasting using GAMs

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Forecasting using GAMs

Description

Returns forecasts and other information for GAMs.

Usage

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

Arguments

object	An object of class gamFit. Usually the result of a call to model_gam.
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 = 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, ]

# Predictors taken as non-linear variables
s.vars <- colnames(sim_data)[3:6]

# Predictors taken as linear variables
linear.vars <- colnames(sim_data)[7:8]

# Model fitting
gamModel <- model_gam(data = sim_train,
                      yvar = "y",
                      s.vars = s.vars,
                      linear.vars = linear.vars)

forecast(gamModel, newdata = sim_test)

smimodel documentation built on April 8, 2026, 5:06 p.m.