Forecasting and optimising decisions via Bayesian optimisation

Project Level: Honours, Masters, PhD

Probabilistic forecasting associates a probability of occurrence with the predicted values, making it a useful technique for determining decisions based on the level of risk one can take. It is a powerful technique that unlike point forecast gives you a complete view of the future unknown values. In this project, we aim to use Bayesian approaches for probabilistic forecasting to predict the demand for products/services and accordingly determine a better decision whatever that may be, e.g., inventory of product, or optimal allocation of resources. We investigate the association between these two using real-world data.