Description: Temporal Context Models are analytical approaches that integrate the temporal dimension into the analysis of multimodal data, which encompasses data from various sources or modalities, such as text, audio, video, and sensors. These models are fundamental for understanding how information evolves over time and how interactions between different modalities can change based on temporal context. By considering time as a critical factor, these models allow for a richer and more accurate interpretation of data, facilitating the detection of patterns and trends that might go unnoticed in static analysis. The main characteristics of Temporal Context Models include the ability to capture the dynamics of data, the integration of different modalities into a coherent framework, and the adaptation to contextual changes over time. Their relevance lies in their application across various fields, including artificial intelligence, social media analysis, health monitoring, and human-computer interaction, where time plays a crucial role in interpretation and decision-making.
