# F-TransR: A sports event revenue prediction model integrating multi-modal and time-series data

## TL;DR

F-TransR is a new model that improves sports event revenue predictions by combining real-time and historical data more effectively than previous methods.

## Contribution

F-TransR introduces a novel Transformer-based model with modules for real-time data, time-series modeling, and cross-modal interaction.

## Key findings

- F-TransR outperforms state-of-the-art models on Kaggle and Reddit datasets with reduced MSE and MAPE and increased R2.
- The model shows strong robustness and scalability for real-world multimodal revenue prediction tasks.

## Abstract

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.