Mobility services require accurate demand prediction in both space and time to effectively
manage fleet rebalancing, provide quick on-demand responses, and enable advanced ride …

Forecast reconciliation is a post-forecasting process that involves transforming a set of
incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for …

Platform businesses operate on a digital core, and their decision-making requires high-
dimensional accurate forecast streams at different levels of cross-sectional (eg …

Renewable energy generation is of utmost importance for global decarbonization.
Forecasting renewable energies, particularly wind energy, is challenging due to the inherent …

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has
organized an annual influenza forecasting challenge with the motivation that accurate …

Carbon emissions and market prices forecasts of the power grid are of great importance for
all electricity traders and consumers. Both forecasts enable flexible demand scheduling …

In this paper, we propose a novel approach to improving forecasts of stock market indexes
by considering common stock prices as hierarchical time series, combining clustering with …

Forecast reconciliation ensures forecasts of time series in a hierarchy adhere to aggregation
constraints, enabling aligned decision making. While forecast reconciliation can enhance …

Reconciliation has become an essential tool in multivariate point forecasting for hierarchical
time series. However, there is still a lack of understanding of the theoretical properties of …

Independent hierarchical estimations for the branched and aggregated streams of district
heating loads often conflict with each other, leading to significant uncertainties in the daily …