Hierarchical forecasting with constrained optimisation

Project level: Honours, Masters, PhD

Forecasting is the base for a lot of managerial decisions such as inventory control, budget and staff planning, etc. Hierarchical time series are several time series that can be organised in hierarchical levels with respect to different features such as geographical regions, product categories, etc. Hierarchical time series forecasting is needed in many situations as often time series are hierarchical in nature. Overall, this is an important problem, and your work will continue to advance the theory and practice of forecasting and develop highly practical skills. In this project, you will develop constrained optimisation models that can be used to forecast time series according to our needs across different levels. This is empirical research and can be implemented on open-source data of M5 forecasting competition. The data is publicly available on https://www.kaggle.com/c/m5-forecasting-accuracy.