F-TransR: A sports event revenue prediction model integrating multi-modal and time-series data - Guibing You; Kelei Guo; Jie Gao; Hanjie Feng; Wei Zou (PLoS ONE, 2025)
Sports event revenue prediction is a complex, multimodal task that requires effective integration of diverse data sources. Traditional models struggle to combine real-time data streams with historical time-series data, resulting in limited prediction accuracy. To address this challenge, we propose F-TransR, a Transformer-based multimodal revenue prediction model. F-TransR introduces key innovations, including a real-time data stream processing module, a historical time-series modeling module, a novel multimodal fusion mechanism, and a cross-modal interaction modeling module. These modules enable the model to effectively integrate and capture dynamic interactions between multimodal features and temporal dependencies, which previous models fail to handle efficiently. Experimental results demonstrate that F-TransR significantly outperforms state-of-the-art models, including Informer, Autoformer, FEDformer, MTNet, and CrossFormer, on the Kaggle Sports Analytics and Reddit Comments datasets. On the Kaggle dataset, MSE and MAPE are reduced by 6.4% and 2.9%, respectively, while R2 increases to 0.938. On the Reddit dataset, MSE and MAPE decrease by 6.6% and 5.3%, respectively, and R2 improves to 0.854. Compared to existing methods, F-TransR not only improves the interaction efficiency of multimodal features but also demonstrates strong robustness and scalability, providing substantial support for multimodal revenue prediction in real-world applications.