Automatic Time Series Forecasting: The<b>forecast</b>Package for<i>R</i>

TLDR

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. The study describes two automatic forecasting algorithms implemented in the forecast package for R. The package offers an innovations state‑space model for exponential smoothing and a step‑wise ARIMA algorithm, both applicable to seasonal and non‑seasonal data and illustrated on four real series, with additional forecasting utilities. The algorithms are applicable to both seasonal and non‑seasonal data and are compared and illustrated using four real time series.

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.