Automatic Time Series Forecasting: The forecast Package for R

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 smoo...

Full description

Bibliographic Details
Main Authors:	Rob J. Hyndman, Yeasmin Khandakar
Format:	Article
Language:	English
Published:	Foundation for Open Access Statistics 2008-03-01
Series:	Journal of Statistical Software
Subjects:	ARIMA models automatic forecasting exponential smoothing prediction intervals state space models time series R
Online Access:	http://www.jstatsoft.org/v27/i03/paper

id	doaj-9b35f41cb88047e78e3d8edab6cd8d99
record_format	Article
spelling	doaj-9b35f41cb88047e78e3d8edab6cd8d992020-11-24T22:40:34ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602008-03-01273Automatic Time Series Forecasting: The forecast Package for RRob J. HyndmanYeasmin KhandakarAutomatic 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.http://www.jstatsoft.org/v27/i03/paperARIMA modelsautomatic forecastingexponential smoothingprediction intervals state space modelstime seriesR
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Rob J. Hyndman Yeasmin Khandakar
spellingShingle	Rob J. Hyndman Yeasmin Khandakar Automatic Time Series Forecasting: The forecast Package for R Journal of Statistical Software ARIMA models automatic forecasting exponential smoothing prediction intervals state space models time series R
author_facet	Rob J. Hyndman Yeasmin Khandakar
author_sort	Rob J. Hyndman
title	Automatic Time Series Forecasting: The forecast Package for R
title_short	Automatic Time Series Forecasting: The forecast Package for R
title_full	Automatic Time Series Forecasting: The forecast Package for R
title_fullStr	Automatic Time Series Forecasting: The forecast Package for R
title_full_unstemmed	Automatic Time Series Forecasting: The forecast Package for R
title_sort	automatic time series forecasting: the forecast package for r
publisher	Foundation for Open Access Statistics
series	Journal of Statistical Software
issn	1548-7660
publishDate	2008-03-01
description	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.
topic	ARIMA models automatic forecasting exponential smoothing prediction intervals state space models time series R
url	http://www.jstatsoft.org/v27/i03/paper
work_keys_str_mv	AT robjhyndman automatictimeseriesforecastingtheforecastpackageforr AT yeasminkhandakar automatictimeseriesforecastingtheforecastpackageforr
_version_	1725704500952432640

Automatic Time Series Forecasting: The forecast Package for R

Similar Items