Teaching Predictive Audit Data Analytic Techniques: Time-Series Forecasting with Transactional and Exogenous Data

Abstract

Audit data analytics is gaining increasing attention from both audit researchers and practitioners. To provide accounting students with firsthand experience utilizing data analytics, this teaching case showcases the implementation of data analytic techniques to transactional-level data from real-world business practice. Specifically, this case demonstrates the application of seasonal autoregressive integrated moving average (ARIMA) models, utilizing exogenous weather data, to predict daily sales amounts of a wholesale club retailer. The learning objective is to demonstrate this process and teach students to apply predictive data analytics through Python programming and incorporate and utilize exogenous data in sales prediction.