utsf (version 1.3.0)

forecast.utsf: Forecasting a time series

Description

Forecasting a time series

Usage

# S3 method for utsf
forecast(object, h, PI = FALSE, level = 90, ...)

Value

an object of class utsf_forecast with the same components of the model received as first argument, plus several components:

pred

The forecast as an ts object.

lower

Lower limits for prediction interval.

upper

Upper limits for prediction interval.

level

Confidence value associated with the prediction interval

Arguments

object

an object of class utsf embedding a forecasting model for a time series.

h

A positive integer. Number of values to be forecast into the future, i.e., forecast horizon.

PI

If TRUE, prediction intervals are produced using simulation and assuming normally distributed errors.

level

Confidence level for predictions intervals.

...

Other arguments passed to methods

Examples

## Forecast time series using k-nearest neighbors
m <- create_model(USAccDeaths, method = "knn")
f <- forecast(m, h = 12)
f$pred
library(ggplot2)
autoplot(f)

## Using k-nearest neighbors changing the default k value
m <- create_model(USAccDeaths, method = "knn", param = list(k = 5))
forecast(m, h = 12)

## Using your own regression model

# Function to build the regression model
my_knn_model <- function(X, y) {
  structure(list(X = X, y = y), class = "my_knn")
}
# Function to predict a new example
predict.my_knn <- function(object, new_value) {
  FNN::knn.reg(train = object$X, test = new_value, y = object$y)$pred
}
m <- create_model(USAccDeaths, method = my_knn_model)
forecast(m, h = 12)