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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.