Challenges of Combining Forecasts from Correlated Sources

This is a talk given by Prof Yael Grushka-Cockayne as part of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization, https://congreso.us.es/mlneedsmo/, branding the role of Operations Research in Artificial Intelligence with the support of EURO, https://www.euro-online.org.

Abstract:

In this talk, we will explore some challenges with forming a consensus forecast when combining forecasts from multiple sources. We will propose the use of a common correlation heuristic for aggregating point forecasts. The forecast aggregation literature has a long history of accounting for correlation among forecast errors. Theoretically sound methods, however, such as covariance-based weights, have been outperformed empirically in many studies by a simple average or weights that account for forecast error variance but assume no correlation. We offer a heuristic that utilizes a common correlation between the forecasters, reducing the number of parameters to be estimated while still accounting for some level of correlation.

More information

Link to the talk: https://eu.bbcollab.com/guest/c1702ac72a8244e6b0f93cf5befdb42c

Organisers

The organizers of the Online Seminar Series Machine Learning NeEDS Mathematical Optimization
Emilio Carrizosa, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Nuria Gómez-Vargas, IMUS-Instituto de Matemáticas de la Universidad de Sevilla
Thomas Halskov, Copenhagen Business School
Dolores Romero Morales, Copenhagen Business School