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Choosing the size of the calibration set in conformal prediction with ahead::conformalize()

T. Moudiki
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In version 0.39.0 of the ahead package, the conformalize() function has been updated to include a new parameter called split_prob. This parameter allows users to specify the proportion of the data that should be used for calibration when constructing conformal prediction intervals.

0 – Install packages

install.packages("pak")

pak::pak("Techtonique/ahead")

library(ahead)

1 – USAccDeaths

par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
                           h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
                           h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
                           h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = USAccDeaths,
                           h = 20L, split_prob = 0.7)
plot(obj)

2 – AirPassengers

par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
                           h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
                           h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
                           h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = forecast::ets, y = AirPassengers,
                           h = 20L, split_prob = 0.7)
plot(obj)

3 – fdeaths

par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
                           h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
                           h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
                           h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = fdeaths,
                           h = 20L, split_prob = 0.7)
plot(obj)

4 – UKDriverDeaths

par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
                           h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
                           h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
                           h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = UKDriverDeaths,
                           h = 20L, split_prob = 0.7)
plot(obj)

5 – WWWusage

par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
                           h = 20L, split_prob = 0.4)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
                           h = 20L, split_prob = 0.3)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
                           h = 20L, split_prob = 0.6)
plot(obj)
obj <- ahead::conformalize(FUN = ahead::dynrmf, y = WWWusage,
                           h = 20L, split_prob = 0.5)
plot(obj)

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