Description: Temporal data processing refers to the handling and analysis of data that depends on time, which involves the collection, organization, and analysis of information that varies over a specific period. This type of data is fundamental in various disciplines as it allows for the identification of patterns, trends, and behaviors over time. The main characteristics of temporal data processing include sequentiality, where data is recorded in chronological order, and temporal dependency, which implies that data at one point can influence future data. Additionally, temporal data processing requires specific techniques to handle seasonality, trends, and irregularities that may arise in the data. The relevance of this type of processing lies in its ability to provide valuable information that can be used for informed decision-making in various fields, including economics, meteorology, public health, and market analysis, among others. In summary, temporal data processing is an essential tool for understanding how events and variables change over time, allowing analysts and scientists to draw meaningful conclusions from historical and current data.
