Description: Overlapping anomalies refer to situations where multiple anomalies share similar characteristics, making their identification and analysis challenging. This phenomenon is particularly relevant in the field of anomaly detection, where the ability to distinguish between different types of irregularities is crucial for informed decision-making. Anomalies can arise in various contexts, such as in financial fraud detection, health system monitoring, cybersecurity, and network performance analysis. The overlap of characteristics can lead to confusion, as anomalies may resemble each other in terms of behavioral patterns, complicating the task for detection algorithms. Therefore, it is essential to develop advanced methods that allow for the effective decomposition and analysis of these anomalies, using techniques such as machine learning and artificial intelligence. Accurate identification of overlapping anomalies not only enhances the effectiveness of detection systems but also helps prevent significant losses across various industries by enabling a quicker and more appropriate response to irregular situations.
