Description: Temporal models are analytical approaches that incorporate the time dimension into the study and interpretation of data. These models are fundamental for understanding how data evolves over time, allowing researchers and professionals to identify patterns, trends, and behaviors that may not be evident in static analysis. In the context of explainable artificial intelligence (AI), temporal models help break down complex decisions into more understandable components, facilitating the interpretation of results. In the realm of multimodal models, these models can integrate different types of data, such as text, images, and time series, to provide a more comprehensive and contextualized view. Edge inference benefits from temporal models by allowing devices to process data in real-time, which is crucial for many applications. Finally, in machine learning, temporal models are essential for tasks such as time series prediction, where the goal is to anticipate future events based on historical data. In summary, temporal models are powerful tools that enable deeper and more dynamic data analysis, adapting to the changing nature of information in today’s world.
