Deep learning-based approach for forecasting intermittent online sales

Abstract

Abstract Deep Neural Networks (DNN’s) present some of the leading applications of Artificial Intelligence (AI) which have proven suitability on various machine-learning use cases. Forecasting demand of intermittent on-line sales is a task which needs to be carried out automatically for a large number of Stock Keeping Units (SKU’s). This paper discusses the intermittent online sales and proposes an AI-based model for forecasting demand. We provide empirical evidence by utilizing data from 17 different sellers with approximately 3000 orders in total. Our findings indicate that thanks to their multi-layered learning structure, the DNN’s can provide up to 35% better accuracy than the classic models such as Moving Average, Exponential Smoothing, Croston’s method and ARIMA. Also, it was revealed that the time between orders’ arrivals follow Exponential distribution and the order sizes also generally follow Exponential distribution. Thus, most of the time, Poisson Exponential distribution can be used for modelling intermittent sales process through online platforms. The analyses show that Poisson Exponential distribution can generate values close to real sales with less than 7% error margin with real data.

Keywords

• Deep learning
• Forecasting
• Artificial intelligence
• e-Commerce
• Compound distributions