# Hierarchical forecasting with a top-down alignment of independent level forecasts

arXiv: https://arxiv.org/abs/2103.08250 · HTML: https://mlsanity.com/paper/2103.08250/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Forecast with Forecasts: Diversity Matters](https://mlsanity.com/paper/2012.01643/) (2020)
- [Intermittent Demand Forecasting with Renewal Processes](https://mlsanity.com/paper/2010.01550/) (2020)

## Built on by (most cited first, 6 of 6)

- [Zero-shot Forecasting by Simulation Alone](https://mlsanity.com/paper/2601.00970/) (2026)
- [Hierarchical transfer learning with applications for electricity load forecasting](https://mlsanity.com/paper/2111.08512/) (2021)
- [On the retraining frequency of global models in retail demand forecasting](https://mlsanity.com/paper/2505.00356/) (2025)
- [Post-Training in Time Series Foundation Models: A Unifying Framework](https://mlsanity.com/paper/2607.20002/) (2026)
- [Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning](https://mlsanity.com/paper/2402.09033/) (2024)
- [Global tree forecasters collapse at the hierarchical aggregate: a five-panel failure characterization](https://mlsanity.com/paper/2609.27912/) (2026)

## Abstract (arXiv)

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 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 approach N-BEATS for continuous time series at the top levels and a widely used tree-based algorithm LightGBM for the intermittent time series at the bottom level. The hierarchical-forecasting-with-alignment approach is a simple yet effective variant of the bottom-up method, accounting for biases that are difficult to observe at the bottom level. It allows suboptimal forecasts at the lower level to retain a higher overall performance. The approach in this empirical study was developed by the first author during the M5 Forecasting Accuracy competition, ranking second place. The method is also business orientated and could benefit for business strategic planning.