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Multimodal demand forecasting aims at predicting product demand utilizingvisual, textual, and contextual information. This paper proposes a method formultimodal product demand forecasting using convolutional, graph-based, andtransformer-based architectures. Traditional approaches to demand forecastingrely on historical demand, product categories, and additional contextualinformation such as seasonality and events. However, these approaches haveseveral shortcomings, such as the cold start problem making it difficult topredict product demand until sufficient historical data is available for aparticular product, and their inability to properly deal with categorydynamics. By incorporating multimodal information, such as product images andtextual descriptions, our architecture aims to address the shortcomings oftraditional approaches and outperform them. The experiments conducted on alarge real-world dataset show that the proposed approach effectively predictsdemand for a wide range of products. The multimodal pipeline presented in thiswork enhances the accuracy and reliability of the predictions, demonstratingthe potential of leveraging multimodal information in product demandforecasting.

Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters