Project Level: Honours, Masters

Hierarchical Forecasting has found many applications in real world. Hierarchical time series refers to a collection of time series that have a natural and structural connection, e.g, time series are gathered across different locations such as sales across different stores and states in a country. Research shows that we can leverage the information on sales in one store in a particular location and use that to forecast the sales for another store. This is known as cross-learning in research. This project aims to use optimisation methods like linear and integer programming in the setting of hierarchical time series, to develop an end-to-end algorithm that is able to forecast the entire series in an optimal way.

Project members