Long range forecasts are the starting point of many decision support systems that need to draw inference from high-level aggregate patterns on forecasted values. State of the art time-series forecasting methods are either subject to concept drift on long-horizon forecasts, or fail to accurately predict coherent and accurate high-level aggregates. In this work, we present a novel probabilistic forecasting method that produces forecasts that are coherent in terms of base level and predicted aggregate statistics. We achieve the coherency between predicted base-level and aggregate statistics using a novel inference method. Our inference method is based on KL-divergence and can be solved efficiently in closed form. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains.
翻译:长距离预测是许多决策支持系统的起点,需要从预测值的高层次综合模式中推断出。最先进的时间序列预测方法要么在长方位预测中容易出现概念漂移,要么无法准确预测一致和准确的高层次总量。在这项工作中,我们提出了一个新颖的概率预测方法,根据基准水平和预测总量统计数据得出一致的预测。我们使用新的推理方法实现预测基水平和汇总统计数据之间的一致性。我们的推论方法以KL-维度为基础,可以封闭方式有效解决。我们表明,我们的方法改善了对三个不同领域真实数据集的预测,即基础水平和看不见总量的预测。