Abstract
A geometric interpretation is developed for so-called reconciliation methodologies used to forecast time series that adhere to known linear constraints. In particular, a general framework is established nesting many existing popular reconciliation methods within the class of projections. This interpretation facilitates the derivation of novel results that explain why and how reconciliation via projection is guaranteed to improve forecast accuracy with respect to a specific class of loss functions. The result is also demonstrated empirically. The geometric interpretation is further used to provide a new proof that forecast reconciliation results in unbiased forecasts provided the initial base forecasts are also unbiased. Approaches for dealing with biased base forecasts are proposed and explored in an extensive empirical study on Australian tourism flows. Overall, the method of bias-correcting before carrying out reconciliation is shown to outperform alternatives that only bias-correct or only reconcile forecasts.
Anastasios N. Panagiotelis
Associate Professor of Business Analytics
My research interests include applied statistics and data science.