A Benchmarking Approach to Forecast Combination
提出一种基于统计模型的方法，将外推模型（如ARIMA时间序列模型）的预测与同一序列其他独立来源的预测进行最优组合，结合各预测方法的优势，并通过季节性ARIMA模型和实证例子进行说明。
This article is concerned with the development of a statistical model-based approach to optimally combine forecasts derived from an extrapolative model, such as an autoregressive integrated moving average (ARIMA) time series model, with forecasts of a particular characteristic of the same series obtained from independent sources. The methods derived combine the strengths of all forecasting approaches considered in the combination scheme. The implications of the general theory are investigated in the context of some commonly encountered seasonal ARIMA models. An empirical example to illustrate the method is included.