Automatic Time Series Forecasting: The forecast Package for R

2008-01-01articleJournal of Statistical Software3,538 citationsdiamond

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Data Analysis with 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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References

1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root

1992Testing the null hypothesis of stationarity against the alternative of a unit root

2006Another look at measures of forecast accuracy

1972Forecasting and Stock Control for Intermittent Demands

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