Sales Forecasting for Pricing Strategies Based on Time Series and Learning Techniques

Abstract

Time series exist in a wide variety of domains, such as market prices, healthcare and agriculture. Modelling time series data enables forecasting, anomaly detection, and data exploration. Few studies compare technologies and methodologies in the context of time series analysis, and existing tools are often limited in functionality. This paper focuses on the formulation and refinement of pricing strategies in mass retail, based on learning methods for sales forecasting and evaluation. The aim is to support BOOPER, a French startup specializing in pricing solutions for the retail sector. We focus on the strategy where each model is refined for a single product, studying both ensemble and parametric techniques as well as deep learning. To use these methods a hyperparameter setting is needed. The aim of this study is to provide an overview of the sensitivity of product sales to price fluctuations and promotions. The aim is also, to adapt existing methods using optimized machine and deep learning models, such as the Temporal Fusion Transformer (TFT) and the Temporal Convolutional Network (TCN), to capture the behaviour of each product. The idea is to improve their performance and adapt them to the specific requirements. We therefore provide an overview and experimental study of product learning models for each dataset, enabling informed decisions to be made about the most appropriate model and tool for each case.