ahead

Univariate and multivariate time series forecasting, with uncertainty quantification.

HitCount

Installation for R (Python is here)

  • 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")

  • 2nd method: from Github

    In R console:

    devtools::install_github("Techtonique/ahead")

Demo

For univariate and multivariate time series.

1 - Univariate time series

1 - 1 Example 1: with dynrmf (type ?dynrmf in R console for more details) and Random Forest 
 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))
1 - 2 Example 2: with dynrmf and Support Vector Machines 
 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.

2 - Multivariate time series

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")

Contributing

Your contributions are welcome. Please, make sure to read the Code of Conduct first.

License

BSD 3-Clause © Thierry Moudiki, 2019.