Convolutional Neural Network for Demand Forecasting

Many retailers are interested in forecasting demand for the products they sell. Deloitte has used machine learning methods to tackle this problem in the past. However, this requires the creation of hand-crafted features based on product sales data, which is a costly and time-intensive process. Using alternative models to perform this task would remove the need for laborious data manipulation. It will also allow model enhancements to scale across many clients rather than requiring from-scratch data manipulation for each new client. Hence, this project will involve the development of a new machine learning model to predict product demand. The model will be trained using historical sales data. Iterative stages of model architecture and fine-tuning will give rise to the final model. Various enhancements to the model architecture will be explored. The main objective is for the final model to be integrated into a modular demand prediction solution at Deloitte.

Faculty Supervisor:

Huaxiong Huang;Arvind Gupta

Student:

Sasha Nanda

Partner:

Deloitte

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto