Decision use cases that reflect uncertain input parameters or potentially uncertain causal relationships often rely on forecasting methods to help provide practical insight.
Presentations in this track include:
An Inexpensive Machine Learning Approach for Robust Forecasting, Or How to Fix the Forecasting Models that the Pandemic Broke
Speaker: Miguel Anjos, Chair of Operational Research at the School of Mathematics, University of Edinburgh
Large Data S-Curves for Construction Project Estimation and Comparative Analysis
Speaker: Miles Porter, Lead Data Scientist in the Central AI group at Trimble, Inc.