Ross Hollyman

TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.

TL;DR: This work establishes a direct link between the nascent Forecast Reconciliation literature and the extensive work on Forecast Combination, and clarifies for the first time how unbiased forecasts for the entire collection can be generated from base forecasts made at any level of the hierarchy.

TL;DR: In this paper, the authors provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organize, and evaluate forecasts.

TL;DR: In this paper , the authors examined the problem of making reconciled forecasts of large collections of related time series through a behavioural / Bayesian lens. But their approach explicitly acknowledges and exploits the connectedness of the series in terms of time-series characteristics and forecast accuracy as well as hierarchical structure.