A Dynamic Prediction Model for Business Cycle Value Based on Multimodal Data Fusion and Deep Learning

The major issues with business cycle value prediction are that the data sources are heterogeneous, complex spatiotemporal dependencies, and a lack of long-term prediction accuracy. The model of dynamic prediction, which is developed in this paper, is founded on the fusion of multimodal data and deep learning and incorporates four kinds of heterogeneous data, financial indicators, market transactions, macroeconomic data, and textual sentiment. The former applies a Temporal Convolutional Network (TCN) to learn the temporal features of each modality and a Multi-Head Attention (MHA) mechanism to learn cross-modal interaction relationships. Second, gated fusion unit is intended to weigh and fuse heterogeneous features dynamically and Bidirectional Long Short-Term Memory (BiLSTM) network is employed to model cycle evolution law. Lastly, residual connections with Dropout regularization are also added to enhance the generalization capacity of the model. The model on an empirical dataset of 3500 companies in the 2010-2024 period had a mean absolute percentage error (MAPE) of 6.2% and root mean square error (RMSE) of 0.043 in the expansion phase and 8.7% in the contraction phase, giving it a useful tool and theoretical foundation on dynamic monitoring of business cycle and corporate decision-making.