Collections of time series formed via aggregation are prevalent in many fields. These are
commonly referred to as hierarchical time series and may be constructed cross-sectionally …

Obtaining accurate probabilistic forecasts is an important operational challenge in many
applications, like energy management, climate forecast, supply chain planning, and …

This paper focuses on demand forecasting for multichannel companies that rely on several
distribution channels. Usually, multichannel distribution complicates forecasting because the …

Many authors have highlighted the importance of reliable forecasts in industry–be it for
demand and capacity planning or corporate strategy. Hierarchical time series, where higher …

This thesis is concerned with designing efficient methods to incorporate known structure in
machine learning models. Structure arises either from problem formulation (eg physical …

This study addresses the challenge of optimizing discount strategies for a consumer goods
manufacturer in the snack industry, aiming to enhance direct sales to retailers and gain a …

Hierarchical time series forecasting requires not only prediction accuracy but also
coherency, ie, forecasts add up appropriately across the hierarchy. Recent literature has …

Obtaining accurate probabilistic forecasts is an operational challenge in many applications,
such as energy management, climate forecasting, supply chain planning, and resource …

Hierarchical forecasting (HF) methods are extensively utilized for precise decision-making
by providing coherent forecasts across various levels. Traditionally, statistical models have …

Novel learning algorithms have enhanced our ability to acquire knowledge solely from past
observations of single events to learn from the observations of several related events. This …