Description: Temporal Pattern Mining is the process of discovering patterns in temporal data, which involves analyzing sequences of data over time to identify trends, cycles, and recurring behaviors. This approach is based on the premise that data is not static but evolves and changes over time, allowing analysts to extract valuable information that can be used for decision-making. Temporal pattern mining relies on predictive analytics and unsupervised learning techniques, where the goal is not only to understand past behavior but also to forecast future events. Key characteristics include the ability to handle large volumes of data, identify complex patterns, and apply algorithms that do not require prior labels to classify information. This discipline is fundamental in various fields such as economics, healthcare, marketing, and meteorology, where time plays a crucial role in data interpretation. The relevance of temporal pattern mining lies in its ability to transform data into knowledge, enabling organizations to anticipate changes and adapt proactively.
