A Trainable Reconciliation Method for Hierarchical Time-Series

ITISE (7th International Conference on Time Series and Forecasting)


Davide Burba, Trista Chen




In numerous applications, it is required to produce forecasts for multiple time-series at different hierarchy levels. An obvious example is given by the supply chain in which demand forecasting may be needed at a store, city, or country level. The independent forecasts typically do not add up properly because of the hierarchical constraints, so a reconciliation step is needed.

In this paper, we propose a new general, flexible, and easy-to-implement reconciliation strategy based on an encoder-decoder neural network. By testing our method on four real world datasets, we show that it can consistently reach or surpass the performance of existing methods in the reconciliation setting.


  • Hierarchical Time-Series
  • Neural Networks
  • Reconciliation
  • Forecasting