Autoregressive moving average (ARMA) models remain a competitive tool for forecasting low signal-to-noise ratio time series, due to their flexibility, low complexity and physical plausibility. They predict the next observation in a time series as a linear combination of a number of previous observations as well as a number of hidden (latent) random innovations. The AutoARIMA package remains a staple benchmarking tool against which forecasting techniques must be compared. The AutoARIMA package automatically selects the relevant number of past observations to use when forecasting the next observation in a time series, but does so using relatively old and approximate statistical techniques.