Univariate and multivariate time series forecasting, with uncertainty quantification.
Installation for R (Python is here)
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1st method: from R-universe
In R console:
options(repos = c( techtonique = 'https://techtonique.r-universe.dev', CRAN = 'https://cloud.r-project.org')) install.packages("ahead")
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2nd method: from Github
In R console:
devtools::install_github("Techtonique/ahead")
For univariate and multivariate time series.
require(fpp)
par(mfrow=c(3, 2))
plot(dynrmf(USAccDeaths, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(AirPassengers, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(lynx, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(WWWusage, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(Nile, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = randomForest::randomForest,
fit_params = list(ntree = 50), predict_func = predict)) require(e1071)
par(mfrow=c(2, 2))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "linear"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "polynomial"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "radial"), predict_func = predict))
plot(dynrmf(fdeaths, h=20, level=95, fit_func = e1071::svm,
fit_params = list(kernel = "sigmoid"), predict_func = predict))For more examples on dynrmf, you can read this blog post.
With ridge2f (type ?ridge2f in R console for more details), the model from :
Moudiki, T., Planchet, F., & Cousin, A. (2018). Multiple time series forecasting using quasi-randomized functional link neural networks. Risks, 6(1), 22.
require(fpp)
print(ahead::ridge2f(fpp::insurance)$mean)
print(ahead::ridge2f(fpp::usconsumption)$lower)
res <- ahead::ridge2f(fpp::insurance, lags=2)
par(mfrow=c(1, 2))
plot(res, "Quotes")
plot(res, "TV.advert")Your contributions are welcome. Please, make sure to read the Code of Conduct first.
git remote set-url origin https://username:[email protected]/xxx/repo.gitgit remote set-url origin https://[email protected]/Techtonique/ahead.gitBSD 3-Clause © Thierry Moudiki, 2019.