Forecast to Optimise Decisions

Abstract

Forecasting and decision optimisation are among the most powerful tools in data-driven decision-making under uncertainty. From retail demand planning to energy load scheduling, the ability to forecast an uncertain future as accurately as possible and optimise actions accordingly is critical. However, literature suggests greater forecast accuracy does not always guarantee better decisions. In this talk, I will highlight why integrating forecasting with decision making is important and introduce methods that account for both forecast accuracy and downstream performance. I will illustrate these ideas through hierarchical time series forecasting, where producing coherent forecasts across multiple levels of aggregation is required for effective decision making at various levels of a system.

Bio

Dr Mahdi Abolghasemi is a Senior Lecturer in Data Science at Queensland University of Technology. His current research is focused on time series forecasting, machine learning, and decision-making under uncertainty, with applications in supply chains and energy systems. He has worked with leading energy, retail, and manufacturing companies in Australia, delivering data science models that are now in production and have driven significant cost savings. He is an active member of the International Institute of Forecasters and serves on the editorial board of the International Journal of Forecasting.