Hierarchical forecasting with a top-down alignment of independent level forecasts. (arXiv:2103.08250v4 [stat.ML] UPDATED)

Hierarchical forecasting with intermittent time series is a challenge in both
research and empirical studies. Extensive research focuses on improving the
accuracy of each hierarchy, especially the intermittent time series at bottom
levels. Then hierarchical reconciliation could be used to improve the overall
performance further. In this paper, we present a
\emph{hierarchical-forecasting-with-alignment} approach that treats the bottom
level forecasts as mutable to ensure higher forecasting accuracy on the upper
levels of the hierarchy. We employ a pure deep learning forecasting …

arxiv forecasting independent ml