Combination of multi-horizon models for demand forecasting

This project aims to develop a retail demand forecasting model that can both handle the long-term and short-term forecasting, and adjust its parameters as more data come in. General long-term prediction models are relatively precise because the context often remains same over time, but can not quickly adapt to unforeseen events, like the global pandemics. It is then necessary to develop model with multi-horizon perspectives. With the understanding and results achieved by this project, accurate and real-time improvement solutions could be proposed and implemented. It therefore makes economic sense to delve into understanding the travel behaviors of customers and then adjusting the retail practices if unforeseen events occur. This project is expected to produce practical results benefiting the public in the form of improved customer experience, increased incomes, and analysis of COVID-19 propagation containment, etc.

Faculty Supervisor:

Lijun Sun

Student:

Dingyi Zhuang

Partner:

ExPretio Technologies Inc

Discipline:

Engineering - civil

Sector:

Professional, scientific and technical services

University:

McGill University