Basics of Anomaly Detection with Multivariate Gaussian Distribution

Our innate ability to recognize patters allows us to use this skill in filling-in gaps or predicting what is going to happen next. Occasionally, however, something happens that does not fit our expectation and does not fall into our perception of a pattern. We call such occurrences anomalies. If we are trying to predict something, we may want to exclude anomalies from our training data. Or perhaps we want to identify anomalies to help make our life better. In either case, anomaly detection techniques can prove to be useful and applicable in most industries and subject areas. This article will guide you through the basics of anomaly detection and implementation of statistical anomaly detection model. <h1>What is anomaly detection?</h1> In general terms, anomaly detection refers to the process of identifying phenomena that is out of ordinary. The goal of anomaly detection is to identify events, occurrences, data points, or outcomes that are not in line with our expectations and do not fit some underlying pattern. Hence, the key to implementing anomaly detection is to understand the underlying pattern of expected events. If we know the pattern of the expected, we can use it to map the never-before-seen data points; if our mapping is not successful and our new data point falls outside of our expected pattern, it’s probable that we have found our anomaly. <a href="https://towardsdatascience.com/the-basics-of-anomaly-detection-65aff59949b7">Click Here</a>