In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal scales, and constraints imposed by data privacy protection, traditional single-source modeling approaches are unable to fully exploit multisource information. To address this issue, a federated multimodal prediction framework for complex market systems, termed Federated Market-Sensor Transformer (FMST), is proposed. In this framework, data originating from different information sources are uniformly modeled as multimodal time series. A multimodal market-sensor representation module is constructed to perform unified feature encoding, and a cross-modal Transformer fusion architecture is employed to characterize dynamic interaction relationships among different information sources. Meanwhile, a federated collaborative learning mechanism is introduced during the training phase, enabling multiple data nodes to perform collaborative model optimization without sharing raw data. In this manner, data privacy can be preserved while improving the cross-region generalization capability of the model. Systematic experimental evaluation is conducted on the constructed multimodal market-sensor dataset. The experimental results demonstrate that the proposed method consistently outperforms traditional statistical models and deep learning approaches across multiple evaluation metrics. In the main prediction experiment, FMST achieves a root mean square error (RMSE) of $0.1136$, a mean absolute error (MAE) of $0.0832$, and a coefficient of determination $R^2$ of $0.8517$, while the direction prediction accuracy reaches $74.56\%$, clearly outperforming baseline models including ARIMA, LSTM, Temporal CNN, Transformer, and FedAvg-LSTM. In the cross-region generalization experiment, FMST maintains strong performance, achieving an RMSE of $0.1242$, an MAE of $0.0908$, an $R^2$ value of $0.8261$, and a direction prediction accuracy of $72.48\%$. The ablation study further indicates that the three core components—multimodal market-sensor representation, cross-modal Transformer fusion, and federated collaborative learning—each make important contributions to the overall model performance. These experimental findings demonstrate that the proposed method can effectively integrate multisource market information and significantly enhance the prediction capability for complex market dynamics, providing a new technical pathway for the application of artificial intelligence-driven multimodal sensing systems in economic data analysis.

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems.

Urban traffic congestion prediction is an important problem in smart city sensing and intelligent traffic governance. Existing methods mostly rely on single-source traffic flow sensing data or static road topology, making it difficult to sufficiently characterize the dynamic congestion propagation process driven by multisource sensing information, such as traffic flow, vehicle trajectories, road images, public transportation, meteorological conditions, and sudden events. To address this issue, a spatiotemporal causal graph learning framework based on multisource urban sensing data is proposed for urban traffic state prediction, congestion identification, and explainable early warning. In this framework, traffic flow detector data, GPS trajectories, roadside camera data, public transportation data, weather data, and event records are first fused through a multisource urban sensing data collaborative encoding module, and the influence of low-quality or missing sensing modalities is suppressed using a reliability-aware attention mechanism. Subsequently, time-varying causal propagation relationships among road segments are adaptively learned from historical traffic states, road topology, and external disturbances through a dynamic spatiotemporal causal graph learning module. Finally, spatial diffusion and temporal evolution are jointly modeled by a causality-explanation-driven congestion prediction module, and key congestion sources, propagation paths, and inducing factors are outputs. Experimental results based on multisource traffic sensing data from the main urban area of Hangzhou show that the proposed method achieves MAE values of $3.21$, $3.79$, and $4.48$ in 15-min, 30-min, and 60-min traffic state prediction tasks, respectively, outperforming ARIMA, XGBoost, LSTM, Transformer, STGCN, Graph WaveNet, GMAN, Multimodal Transformer, and the Causal Temporal Graph Network. In the ablation study, the complete model achieves an Accuracy of $0.914$, a Precision of $0.902$, a Recall of $0.889$, an F1 of $0.895$, and an AUC of $0.956$. For congestion identification and early warning under complex scenarios, F1 values of $0.927$, $0.904$, and $0.893$ are achieved under peak-hour, rainy-weather, and traffic-event scenarios, respectively; the corresponding AUC values reach $0.966$, $0.957$, and $0.948$; and the false alarm rate (FAR) values are reduced to $0.061$, $0.072$, and $0.081$. The results indicate that the proposed method can effectively improve traffic state prediction accuracy, congestion early warning reliability, and model interpretability under multisource urban sensing conditions, thereby providing an effective technical pathway for AI-driven intelligent traffic sensing.