A Simple Method of Computing Prediction Intervals for Time Series Forecasts
提出一种基于经验误差方差和切比雪夫不等式的预测区间计算方法，假设很少，在M竞赛的111个序列中，95%预测区间实际覆盖了95.8%的样本外观测值。
Theoretical approaches to computing prediction intervals require strong assumptions that do not appear to hold in practice. This paper presents an empirical approach to prediction intervals that assumes very little. During model-fitting, variances of the errors are computed at different forecast leadtimes. Using these variances, the Chebyshev inequality is applied to determine prediction intervals. Empirical evidence is presented to show that this approach gives reasonable results. For example, using the 111 series in the M-competition, 95% prediction intervals actually contain 95.8% of post-sample observations.