Multimodal Temporal Fusion Transformers Are Good Product Demand
  Forecasters
Maarten Sukel Stevan Rudinac Marcel Worring
Multimodal demand forecasting aims at predicting product demand utilizing
visual, textual, and contextual information. This paper proposes a method for
multimodal product demand forecasting using convolutional, graph-based, and
transformer-based architectures. Traditional approaches to demand forecasting
rely on historical demand, product categories, and additional contextual
information such as seasonality and events. However, these approaches have
several shortcomings, such as the cold start problem making it difficult to
predict product demand until sufficient historical data is available for a
particular product, and their inability to properly deal with category
dynamics. By incorporating multimodal information, such as product images and
textual descriptions, our architecture aims to address the shortcomings of
traditional approaches and outperform them. The experiments conducted on a
large real-world dataset show that the proposed approach effectively predicts
demand for a wide range of products. The multimodal pipeline presented in this
work enhances the accuracy and reliability of the predictions, demonstrating
the potential of leveraging multimodal information in product demand
forecasting.