Variational Autoencoder-Based Framework for Retail Sales Prediction

Accurate retail sales prediction is crucial for supporting the intelligent operations and management of a retail sales organization. For example, intelligent inventory replenishment based on forecasted sales can help reduce inventory backlog and turnover periods, improving operational efficiency. This paper presents a variational autoencoder (VAE)-based framework for retail sales prediction, with three unique contributions. First, the framework leverages the clustering properties of the VAE in latent space to enhance feature learning. The data clustering module integrates correlation information across different samples to effectively extract both local and global features.Second, we design a restructured VAE to capture high-level local and global features essential for sales prediction. The use of multiple self-adaptive priors and corresponding posteriors diversifies the posterior space, facilitating effective learning of characteristic distributions across samples. This approach enhances the extraction of useful features from the original data. Finally, to mitigate abnormal prediction outcomes, we incorporate prior knowledge to adjust predictions that may be affected by the model’s limited fitting capacity or insufficient training data in certain cases. Extensive experiments conducted in collaboration with a national retail chain in China demonstrate that our method outperforms state-of-the-art baseline methods and is practical for various operational and management tasks.