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All Projects → Techtonique → ahead

Techtonique / ahead

Licence: BSD-3-Clause-Clear license
Univariate and multivariate time series forecasting

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r
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Labels

machine-learningtime-seriesstatistical-learning

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ahead

Time series (univariate and multivariate) forecasting

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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].