Description: Sequence analysis is the process of examining data series over time to identify patterns, trends, and anomalies. This approach is fundamental in various disciplines, from computational biology to fraud detection in financial systems. In the context of artificial intelligence (AI), sequence analysis relies on advanced algorithms that enable the processing of large volumes of data and the extraction of relevant information. The main characteristics of this analysis include the ability to handle temporal data, the identification of unusual behaviors, and the prediction of future events based on historical patterns. The relevance of sequence analysis lies in its application in improving decision-making, optimizing processes, and preventing risks, making it an essential tool in today’s world, where data is increasingly abundant and complex.
History: Sequence analysis has its roots in molecular biology, where it was first used in the 1970s to study DNA sequences. With the advancement of computing and the development of machine learning algorithms in the following decades, its application expanded to other fields such as economics and cybersecurity. In the 1990s, sequence analysis began to integrate with artificial intelligence techniques, enabling more effective and accurate anomaly detection.
Uses: Sequence analysis is used in a variety of fields, including biology for genome sequencing, in finance for fraud detection, and in cybersecurity to identify anomalous behaviors in networks. It is also applied in time series analysis in economics and in predicting failures in industrial systems.
Examples: A practical example of sequence analysis is the use of machine learning algorithms to detect fraudulent transactions in real-time in banking systems. Another example is the analysis of sensor data in industrial machinery to predict failures before they occur, allowing for more effective preventive maintenance.
