Work

Aggregate Sales Forecasting Based on Spatial Correlation in Retail

Year: 2025

Type: article

Abstract: Aggregated sales forecasting is an important research topic in the highly competitive retail industry. With the availability of different data sources, various techniques and models have been proposed for aggregated sales forecasting. However, existing methods often overlook the spatial correlation in sales between neighboring retailers. In this paper, we propose a new framework for aggregated sales forecasting based on the deep learning technique ConvLSTM with an attention mechanism to solve this challenge. In the new framework, ConvLSTM is utilized to fully leverage spatially relevant information from adjacent retailers, while the attention mechanism is employed to capture spatial dependencies and select the most pertinent data from spatial inputs. Furthermore, a spatial sliding window technique is designed to augment the sample size. To validate the efficacy of our proposed framework, we conducted experiments using real-world retail sales data and compared our model with established benchmarks. Additionally, we conducted an ablation study to assess the contributions of key components, including the attention mechanism and spatial data augmentation. The experimental results demonstrate that our proposed model effectively improves the prediction performance, offering a novel approach to aggregate sales forecasting for both industry and academia.

Source: Journal of theoretical and applied electronic commerce research

DOI: 10.3390/jtaer20040334

Authors: Bing Zhu, Zhengqian Sun, Seppe vanden Broucke, Keyi Lan, Duoxi Xiao

Institutions: Sichuan University, Ghent University

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,207

Funder: Fundamental Research Funds for the Central Universities