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 based on KL-divergence that 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. (\href{https://github.com/pratham16cse/AggForecaster}{Project URL})
翻译:长期预测是许多决策支持系统的起点,这些系统需要从预测值的高级总体模式中作出推断。最新时间序列预测方法要么在长正数预测中出现概念漂移,要么无法准确预测一致和准确的高水平总量。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。我们利用基于KL-动态的新型推论方法实现预测基水平和汇总统计数据之间的一致性,这种推论方法可以以封闭形式有效解决。我们表明,我们的方法改进了基础水平和不可见汇总在三个不同领域真实数据集上的预测性。 (href{https://github.com/pratham16cse/AggForecater}项目URLU)