bridgr (version 0.1.1)

forecast.bridge: Forecasting from a bridge object

Description

This function is used to forecast the target variable using the fitted bridge model.

Usage

# S3 method for bridge
forecast(object, xreg = NULL, ...)

Value

An object of class "forecast". An object of class "forecast" is a list usually containing at least the following elements:

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

mean

Point forecasts as a time series

lower

Lower limits for prediction intervals

upper

Upper limits for prediction intervals

level

The confidence values associated with the prediction intervals

x

The original time series (either object itself or the time series used to create the model stored as object).

residuals

Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.

fitted

Fitted values (one-step forecasts)

forecast_set

The forecast set with the forecasted indicator variables used to calculate the forecast of the target variable.

Arguments

object

A bridge object obtained from bridge().

xreg

A tsbox::ts_boxable() series of external regressors. If not supplied by the user, the forecast set calculated by bridge() will be used. This is useful for made up scenarios.

...

Additional arguments to be passed to the forecast function. Ignored at the moment.

Examples

library(bridgr)

# Example usage
target_series <- suppressMessages(tsbox::ts_tbl(data.frame(
  time = seq(as.Date("2020-01-01"), as.Date("2022-12-01"), by = "quarter"),
  value = rnorm(12)
)))

indic_series <- suppressMessages(tsbox::ts_tbl(data.frame(
  time = seq(as.Date("2020-01-01"), as.Date("2023-01-01"), by = "month"),
  value = rnorm(37)
)))

bridge_model <- suppressMessages(bridge(
  target = target_series,
  indic = indic_series,
  indic_predict = "mean",
  indic_aggregators = "mean",
  indic_lags = 2,
  target_lags = 1,
  h = 1
))

# Forecasting using the bridge model
fcst <- forecast(bridge_model)