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 innovation state space models that underly exponential smoothing methods. The second is based on 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.
Keywords: ARIMA models, automatic forecasting, exponential smoothing, prediction intervals, state space models, time series, R.