Estimation and Prediction of Censored Arrival Processes with Censoring for Replenishable Item Purchases

The aim of the project is to predict future customer demand for repeat-buying items based on available customer purchase records. However, the purchase history for a single customer may not be sufficient to base predictions on. Also, some purchase records might be missing due to sales events at competitorsâ locations. Thus, treating each customer as a replicant of the average customer and averaging inter-purchase times to predict future demand will likely be an inadequate approach. For this project, a generalization of traditional models in marketing research will be studied and a more flexible model that accounts for time-varying model features will be investigated to better model the data generation process to provide accurate forecasts that will bring foreseeable benefits in logistical efficiency.

Faculty Supervisor:

Nancy Reid

Student:

Tianle Chen

Partner:

Rubikloud Technologies Inc.

Discipline:

Statistics / Actuarial sciences

Sector:

Information and communications technologies

University:

Program: