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In the popular R package forecast, there are 2 different types of interfaces:
- A direct interface for functions like
forecast::thetafdoing fitting and inference simultaneously
nile.fcast <- forecast::thetaf(Nile)
plot(nile.fcast)
- An interface for fitting first and then forecasting, like
forecast::ets, where you need to use, in additionforecast::forecast:
fit <- forecast::ets(USAccDeaths)
plot(forecast::forecast(fit))
In this post, I describe how to obtain probabilistic forecasts from R package forecast – and packages that follow a similar philosophy such as forecastHybrid, ahead, etc. –, by using a unified interface (ahead::genericforecast). Then, I present ahead::conformalize, a function that allows to obtain forecasts using the method described in Conformalized predictive simulations for univariate time series (more details can be found in these slides).
0 - Packages
utils::install.packages(c("remotes", "e1071", "forecast", "glmnet"))
remotes::install_github("Techtonique/ahead")
library(ahead)
library(forecast)
y <- fdeaths #AirPassengers #Nile #mdeaths #fdeaths #USAccDeaths
h <- 25L
1 - Generic forecaster (unified interface)
1 - 1 - Using default parameters
par(mfrow=c(2, 2))
plot(ahead::genericforecast(FUN=forecast::thetaf, y, h))
plot(ahead::genericforecast(FUN=forecast::meanf, y, h))
plot(ahead::genericforecast(FUN=forecast::rwf, y, h))
plot(ahead::genericforecast(FUN=forecast::ets, y, h))
par(mfrow=c(2, 2))
plot(ahead::genericforecast(FUN=forecast::tbats, y, h))
plot(ahead::genericforecast(FUN=HoltWinters, y, h))
plot(ahead::genericforecast(FUN=forecast::Arima, y, h))
plot(ahead::genericforecast(FUN=ahead::dynrmf, y, h))
1 - 2 - Using additional parameters
par(mfrow=c(2, 2))
plot(ahead::genericforecast(FUN=ahead::dynrmf, y=y, h=h,
fit_func=e1071::svm, predict_func=predict))
plot(ahead::genericforecast(FUN=ahead::dynrmf, y=y, h=h,
fit_func=glmnet::cv.glmnet, predict_func=predict))
plot(ahead::genericforecast(FUN=forecast::tbats, y=y, h=h,
use.box.cox = TRUE, use.trend=FALSE))
plot(ahead::genericforecast(FUN=forecast::rwf,
y=y, h=h, lambda=1.1))
2 - Conformal prediction
2 - 1 - Using default parameters
y <- USAccDeaths
par(mfrow=c(3, 2))
obj <- ahead::conformalize(FUN=forecast::thetaf, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::meanf, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::rwf, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::ets, y, h); plot(obj)
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN=forecast::auto.arima, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::tbats, y, h); plot(obj)
obj <- ahead::conformalize(FUN=HoltWinters, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::Arima, y, h); plot(obj)
2 - 2 - Using additional parameters
y <- AirPassengers
par(mfrow=c(2, 2))
obj <- ahead::conformalize(FUN=forecast::thetaf, y, h); plot(obj)
obj <- ahead::conformalize(FUN=forecast::rwf, y=y, h=h, drift=TRUE); plot(obj)
obj <- ahead::conformalize(FUN=HoltWinters, y=y, h=h, seasonal = "mult"); plot(obj)
obj <- ahead::conformalize(FUN=ahead::dynrmf, y=y, h=h, fit_func=glmnet::cv.glmnet, predict_func=predict); plot(obj)
2 - 3 - Using other simulation methods (conformal prediction-based)
y <- fdeaths
par(mfrow=c(3, 2))
obj <- ahead::conformalize(FUN=forecast::thetaf, y=y, h=h, method="block-bootstrap"); plot(obj)
obj <- ahead::conformalize(FUN=forecast::rwf, y=y, h=h, drift=TRUE, method="bootstrap"); plot(obj)
obj <- ahead::conformalize(FUN=forecast::ets, y, h, method="kde"); plot(obj)
obj <- ahead::conformalize(FUN=forecast::tbats, y=y, h=h, method="surrogate"); plot(obj)
obj <- ahead::conformalize(FUN=HoltWinters, y=y, h=h, seasonal = "mult", method="block-bootstrap"); plot(obj)
obj <- ahead::conformalize(FUN=ahead::dynrmf, y=y, h=h, fit_func=glmnet::cv.glmnet,
predict_func=predict, method="surrogate"); plot(obj)
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