# Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning

arXiv: https://arxiv.org/abs/2402.09033 · HTML: https://mlsanity.com/paper/2402.09033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Hierarchical forecast reconciliation with machine learning](https://mlsanity.com/paper/2006.02043/) (2020)
- [Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives](https://mlsanity.com/paper/2006.08570/) (2020)
- [Hierarchical forecasting with a top-down alignment of independent level forecasts](https://mlsanity.com/paper/2103.08250/) (2021)
- [Point and probabilistic forecast reconciliation for general linearly constrained multiple time series](https://mlsanity.com/paper/2305.05330/) (2023)
- [End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation](https://mlsanity.com/paper/2212.13706/) (2022)
- [Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues](https://mlsanity.com/paper/2303.17277/) (2023)
- [Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach](https://mlsanity.com/paper/2306.02952/) (2023)

## Built on by (most cited first, 2 of 2)

- [Nonlinear reconciliation: Error reduction theorems](https://mlsanity.com/paper/2507.22500/) (2025)
- [FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R](https://mlsanity.com/paper/2604.27696/) (2026)

## Abstract (arXiv)

Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all levels of the hierarchy to ensure aligned decision making across different planning units such as pricing, product, controlling and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces cross-temporal reconciled forecasts in a direct and automated way through the use of popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle sharing system in New York City.