Automatic time series forecasting: The forecast package for R
Automatic time series forecasting: The forecast package for R
复制标题
DOI：
10.18637/jss.v027.i03
复制
发表时间：
2008-07-01
影响因子：
5.8
通讯作者：
Khandakar, Yeasmin
中科院分区：
文献类型：
--
作者：
Hyndman, Rob J.;Khandakar, Yeasmin
在商业和其他环境中通常需要对大量单变量时间序列进行自动预测。我们描述了 R 预测包中实现的两种自动预测算法。第一种算法基于指数平滑方法下的创新状态空间模型。第二个是使用 ARIMA 模型进行预测的逐步算法。该算法适用于季节性和非季节性数据，并使用四个实时序列进行比较和说明。我们还简要描述了预测包中可用的一些其他功能。
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.