The R Journal: accepted article

This article will be copy edited and may be changed before publication.

SARIMA Analysis and Automated Model Reports with BETS, an R Package PDF download
Talitha F. Speranza, Pedro C. Ferreira and Jonatha A. da Costa

Abstract This article aims to demonstrate how the powerful features of the R package BETS can be applied to SARIMA time series analysis. BETS provides not only thousands of Brazilian economic time series from different institutions, but also a range of analytical tools and educational resources. In particular, BETS is capable of generating automated model reports for any given time series. These reports rely on a single function call and are able to build three types of models (SARIMA being one of them), needing few inputs and outputting rich content. The output varies according to the inputs and usually consists of a summary of the series properties, step by step explanations on how the model was developed and predicitions made by the model, as well as a file containing these predictions. This work focuses on this feature and several other BETS functions that are designed to help the time series modeler. We present them in a thorough case study: the SARIMA approach to model and forecast the Brazilian production of intermediate goods index series.

Received: 2017-09-13; online 2018-12-11
CRAN packages: BETS, forecast, mFilter, urca, seasonal, httr, rvest, RMySQL, rmarkdown, stats, dygraphs
CRAN Task Views implied by cited CRAN packages: TimeSeries, Econometrics, Finance, WebTechnologies, Databases, Environmetrics, MissingData, OfficialStatistics, ReproducibleResearch


CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-070,
  author = {Talitha F. Speranza and Pedro C. Ferreira and Jonatha A. da
          Costa},
  title = {{SARIMA Analysis and Automated Model Reports with BETS, an R
          Package}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-070},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-070/index.html}
}