Forecasting time series and optimising decisions: case study using Walmart data

Project Level: Summer

Project Duration: 6 weeks

Hours of Engagement: 36

Project Description: 

Time series forecasting plays a crucial role in many business decision-making, e.g. you need to forecast sales and accordingly decide what should be your ideal inventory level to maximise your profit and minimise your inventory level. This project is about forecasting a set of related sales time series that have a hierarchical format, meaning that time series are related to each other. For example, consider total sales for a product in which we can have total sales at the top level, sales for each state at the middle level, and sales at each store at the bottom level. There is a logical structure where some of the forecasts at the lower levels (stores) sum up to the higher levels (state and then total). In this project, you will use state-of-the-art forecasting models to forecast all of them accurately and then aim to use linear and mixed integer optimisation methods to develop an algorithm for optimising these forecasts.

Expected Outcomes:

In this project, you will have the opportunity to learn state-of-the-art forecasting models, data science modelling, data science communication, and your thinking skills. There is an opportunity to present at national or international conferences and write articles for top-tier international journals.

Suitable for:

This project is open to applications from postgraduate students with a background in data science/mathematics/statistics and closely related fields. It is essential to be proficient in R or Python and be familiar with linear and mixed integer programming.

Contact for further information:

Dr Mahdi Abolghasemi: m.abolghasemi@uq.edu.au

Please contact Dr Mahdi Abolghasemi and send your CV/Transcript.