Multimodal association refers to the process of associating multiple modalities or types of data in time series analysis. In time series analysis, multiple modalities or types of data can be collected, such as sensor data, images, audio, and text. Multimodal association aims to integrate these different types of data to improve the understanding and prediction of the time series.

For example, in a smart home application, sensor data from temperature, humidity, and motion sensors can be combined with images from cameras to monitor the activities of residents. By analyzing the multimodal data together, the system can detect anomalies or patterns that may not be visible in individual modalities alone.

Multimodal association can be achieved using various techniques, including deep learning models, statistical models, and graph-based models. These models can be trained on the multimodal data to learn the associations and dependencies between the different types of data.