# A Trainable Reconciliation Method for Hierarchical Time-Series

arXiv: https://arxiv.org/abs/2101.01329 · HTML: https://mlsanity.com/paper/2101.01329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [A machine learning approach for forecasting hierarchical time series](https://mlsanity.com/paper/2006.00630/) (2020)
- [A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls](https://mlsanity.com/paper/1906.10586/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions](https://mlsanity.com/paper/2606.07044/) (2026)

## Abstract (arXiv)

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.