Description: Adaptive thresholding is a technique used in anomaly detection that dynamically adjusts the decision threshold based on the specific characteristics of the analyzed data. Unlike fixed threshold methods, which set a constant limit to identify anomalies, adaptive thresholding considers the variability and distribution of data in real-time. This allows for greater sensitivity and accuracy in detecting unusual patterns, as it adapts to changing data conditions. This technique is particularly useful in contexts where data may exhibit significant fluctuations, such as in industrial monitoring, network analysis, or fraud detection. By adjusting the threshold according to the local characteristics of the data, both false positives and false negatives are minimized, thus improving the effectiveness of the detection system. In summary, adaptive thresholding is a powerful tool in the field of anomaly detection, providing a more flexible and precise way to identify atypical behaviors in complex datasets.
