Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Rob J. Hyndman, Yeasmin Khandakar
Title: Automatic Time Series Forecasting: The forecast Package for R
Abstract: Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.

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Paper: Automatic Time Series Forecasting: The forecast Package for R     Download PDF (Downloads: 124790)
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forecasting_1.11.tar.gz: R source package bundle Download (Downloads: 4641; 1MB)
v27i03.R.zip: v27i03.R: R example code from the paper Download (Downloads: 5193; 1KB)
DOI: 10.18637/jss.v027.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.