In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point and density forecasts.

翻译：在这项工作中,我们提出了一个新的密度预测组合框架,根据时间序列特性构建时间变化加权,称为“基于地貌的贝叶西亚预测模型(FEBAMA) ” 。 我们的框架通过贝叶西亚日志预测分数对预测组合中的加权值进行了估算,其中最佳预测组合由历史信息的时间序列特征决定。特别是,我们使用贝叶西亚变量自动选择方法来增加不同特征的重要性。为此,我们的方法与其他黑盒预测组合计划相比,具有更好的可解释性。我们把框架应用于股票市场数据和M3竞争数据。基于我们的结构,一个简单的最大一个前瞻计划超越了基准方法,而巴伊西亚变量选择可以进一步提高点和密度预测的准确性。