Multimodal Association

Multimodal correlation refers to the process of integrating and correlating multiple types of data (such as sensor data, images, audio, and text) in time series analysis. The goal is to enhance the understanding and predictive capabilities of time series by combining different modalities, thereby revealing complex patterns and dependencies that cannot be captured by a single modality alone. Multimodal correlation techniques include deep learning models, statistical models, and graph models, which can learn from and extract useful information from multimodal data, improving the system's perception and decision-making abilities.