A Mixture-Model Approach to Combining Forecasts
提出一种多过程混合模型方法，允许组合预测的权重随时间变化，并自动剔除所有预测都失准的异常数据点，实证表明该方法优于传统及新近的组合方法。
A multiprocess mixture-model approach to combining forecasts from alternative sources is proposed. This approach extends the Granger–Ramanathan method by allowing the weights used in producing the combination forecast to vary over time. In addition, the procedure discounts outlying data points that arise during time periods when all of the competing forecasts miss the mark. An empirical comparison with traditional and more recently proposed combination methods demonstrates that the proposed methodology outperforms these.