SARIMA Analysis and Automated Model Reports with BETS, an R Package

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). The functions need few inputs and output 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, predictions made by the model, and a file containing these predictions. This work focuses on this feature and several other BETS functions that are designed to help in modeling time series. We present them in a thorough case study: the SARIMA approach to model and forecast the Brazilian production of intermediate goods index series.

Talitha F. Speranza , Pedro C. Ferreira , Jonatha A. da Costa

CRAN packages used

BETS, forecast, mFilter, urca, seasonal, httr, rvest, RMySQL, rmarkdown, stats, dygraphs

CRAN Task Views implied by cited packages

TimeSeries, Econometrics, Finance, WebTechnologies, Databases, Environmetrics, MissingData, OfficialStatistics, ReproducibleResearch


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Speranza, et al., "The R Journal: SARIMA Analysis and Automated Model Reports with BETS, an R Package", The R Journal, 2018

BibTeX citation

  author = {Speranza, Talitha F. and Ferreira, Pedro C. and Costa, Jonatha A. da},
  title = {The R Journal: SARIMA Analysis and Automated Model Reports with BETS, an R Package},
  journal = {The R Journal},
  year = {2018},
  note = {},
  doi = {10.32614/RJ-2018-070},
  volume = {10},
  issue = {2},
  issn = {2073-4859},
  pages = {133-147}