Description: The Temporal Sampling Generative Model is an innovative approach in the field of artificial intelligence and machine learning that focuses on generating data over time. Unlike traditional models that may work with static data, this model has the capability to sample and generate sequences of data that evolve over time, making it particularly useful for applications where temporality is a critical factor. This type of model is based on the idea that data can be represented as a series of points in time, thus allowing the capture of temporal dynamics and patterns that may not be evident in a static dataset. The main characteristics of these models include their ability to learn complex distributions and their flexibility to adapt to different types of temporal data, such as time series, text sequences, or audio. The relevance of Temporal Sampling Generative Models lies in their potential to improve the quality of predictions and the generation of synthetic data, opening new possibilities in fields such as simulation, event prediction, and multimedia content generation.