# Reconciliation of probabilistic forecasts with an application to wind power

arXiv: https://arxiv.org/abs/1808.02635 · HTML: https://mlsanity.com/paper/1808.02635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

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

- [SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies](https://mlsanity.com/paper/2302.05650/) (2023)
- [End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation](https://mlsanity.com/paper/2212.13706/) (2022)

## Abstract (arXiv)

New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are evaluated using real data from two wind farms in Crete, an application where it is imperative for optimal decisions related to grid operations and bidding strategies to be based on coherent probabilistic forecasts of wind power. Empirical evidence is also presented showing that probabilistic forecast reconciliation improves the accuracy of both point forecasts and probabilistic forecasts.