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conformalForecast

The R package conformalForecast provides methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID control, and autocorrelated multistep-ahead conformal prediction.

Installation

You can install the development version of conformalForecast from GitHub with:

# install.packages("remotes")
remotes::install_github("xqnwang/conformalForecast")

You can also get the official release version from CRAN with:

install.packages("conformalForecast")

Example

This basic example shows how to perform classical conformal prediction and update the results when new observations become available.

We simulate a series from an AR(2) model, holding back the last five observations to stand in for data that arrives later:

library(conformalForecast)
#> Loading required package: forecast
set.seed(0)
series_all <- arima.sim(n = 1005, list(ar = c(0.8, -0.5)), sd = 1)
series <- head(series_all, 1000)
new_data <- as.numeric(tail(series_all, 5))

Conformal calibration needs a record of past forecast errors. We obtain one with cvforecast(), which refits the model on a rolling forecast origin and stores the out-of-sample errors at every horizon:

far2 <- function(x, h, level) {
  Arima(x, order = c(2, 0, 0)) |>
    forecast(h = h, level = level)
}

fc <- cvforecast(series, forecastfun = far2, h = 3, level = c(80, 95),
                 forward = TRUE, initial = 1, window = 100)

Now calibrate intervals with split conformal prediction, on a rolling window of 100 calibration scores:

scpfc <- conformal(fc, method = "scp", ncal = 100, rolling = TRUE)

When the five held-back observations arrive, update() extends the fit incrementally. Intervals that were already issued do not change:

scpfc_updated <- update(scpfc, new_data = new_data, forecastfun = far2)

Finally, evaluate the intervals by forecast horizon:

# Interval forecast accuracy
accuracy(scpfc_updated, byhorizon = TRUE)
#>        Winkler_95  MSIS_95
#> CV h=1   5.002887 4.830049
#> CV h=2   6.523012 6.297515
#> CV h=3   6.630259 6.397788

# Mean coverage
coverage(scpfc_updated, level = 95)
#>       h=1       h=2       h=3 
#> 0.9503106 0.9476961 0.9400749

# Mean and median interval width
width(scpfc_updated, level = 95, includemedian = TRUE)
#> Mean width:
#>      h=1      h=2      h=3 
#> 4.119936 5.371825 5.406426 
#> 
#> Median width:
#>      h=1      h=2      h=3 
#> 4.074733 5.334842 5.326651

Learn more

• Introduction to conformalForecast: conformal methods, their formulas and tuning arguments, with a comparison of coverage and interval width.
• Updating conformal forecasts with new data: how update() extends a fitted pipeline as observations arrive, and when it is equivalent to refitting.

License

This package is free and open source software, licensed under GPL-3.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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