Package: legion 0.2.1

Ivan Svetunkov

legion: Forecasting Using Multivariate Models

Functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, <doi:10.1177/1471082X0901000401>), Vector ETS (Svetunkov et al., 2023, <doi:10.1016/j.ejor.2022.04.040>) and simulation function for VES.

Authors:Ivan Svetunkov [aut, cre], Kandrika Fadhlan Pritularga [aut]

legion_0.2.1.tar.gz
legion_0.2.1.tar.gz(r-4.5-noble)legion_0.2.1.tar.gz(r-4.4-noble)
legion_0.2.1.tgz(r-4.4-emscripten)legion_0.2.1.tgz(r-4.3-emscripten)
legion.pdf |legion.html✨
legion/json (API)
NEWS

# Install 'legion' in R:
install.packages('legion', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/config-i1/legion/issues9 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.65 score 1 packages 950 downloads 8 exports 22 dependencies

Last updated 2 months agofrom:a4b7ef553f. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-linux-x86_64OKMar 05 2025
R-4.4-linux-x86_64OKMar 05 2025

Exports:auto.vetsis.legionis.legion.simis.ovesovessim.vesvesvets

Dependencies:askpasscurlgenericsgreyboxhttrjsonlitelatticeMASSMatrixmimenloptropensslpracmaR6RcppRcppArmadillosmoothstatmodsystexregxtablezoo

legion: Forecasting Using Multivariate Models

Ivan Svetunkov

Rendered fromlegion.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2021-05-17
Started: 2021-05-17

ves() - Vector Exponential Smoothing

Ivan Svetunkov

Rendered fromves.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2022-02-15
Started: 2021-05-17

vets() - Vector ETS

Ivan Svetunkov

Rendered fromvets.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2022-02-15
Started: 2021-05-17

Citation

To cite package ‘legion’ in publications use:

Svetunkov I, Pritularga K (2025). legion: Forecasting Using Multivariate Models. R package version 0.2.1, https://CRAN.R-project.org/package=legion.

Corresponding BibTeX entry: 

  @Manual{,
    title = {legion: Forecasting Using Multivariate Models},
    author = {Ivan Svetunkov and Kandrika Fadhlan Pritularga},
    year = {2025},
    note = {R package version 0.2.1},
    url = {https://CRAN.R-project.org/package=legion},
  }

Readme and manuals

legion

The package legion implements several multivariate models for purposes of forecasting.

hex-sticker of the legion package for R

Here is the list of the included functions:

  1. ves - Vector Exponential Smoothing.
  2. vets - Vector ETS with PIC taxonomy.
  3. auto.vets - Automatic selection of restrictions for VETS.
  4. sim.ves - simulates data from VES.
  5. oves - occurrence state space vector exponential smoothing model.

Available methods:

  1. AIC, BIC, AICc, BICc;
  2. coefficients;
  3. fitted;
  4. forecast;
  5. actuals;
  6. logLik;
  7. modelType - type of the estimated model;
  8. nobs;
  9. nparam - number of the estimated parameters in the model;
  10. nvariate - number of series in the model;
  11. residuals - the residuals of the model (et in case of additive and log(1+et) for the multiplicative ones);
  12. rstandard, rstudent - standardised and studentised residuals;
  13. outlierdummy - extracts outliers in the model and creates dummy variables for them;
  14. plot - produces several plots for diagnostics purposes. See the documentation for plot.legion();
  15. print;
  16. sigma;
  17. simulate;
  18. summary;

Installation

The stable version of the package is available on CRAN, so you can install it by running:

install.packages("legion")

A recent, development version, is available via github and can be installed using "remotes" in R. First, make sure that you have remotes:

if (!require("remotes")){install.packages("remotes")}

and after that run:

remotes::install_github("config-i1/legion")