Outlier = legitimate data point that’s far away from the mean or median in a distribution.

Anomaly = illegitimate data point that’s generated by a different process than whatever generated the rest of the data

Anomaly detection refers to the problem of ending anomalies in data. While anomaly is a generally accepted term, other synonyms, such as outliers are often used in different application domains. In particular, anomalies and outliers are often used interchangeably.

If this answer help you, please recommend it.

Outlier = legitimate data point that’s far away from the mean or median in a distribution.

Anomaly = illegitimate data point that’s generated by a different process than whatever generated the rest of the data

Anomaly detection refers to the problem of ending anomalies in data. While anomaly is a generally accepted term, other synonyms, such as outliers are often used in different application domains. In particular, anomalies and outliers are often used interchangeably.

If this answer help you, please recommend it.

Hi

Fundamentally there is no difference. Say you have data and you want to build a model of it. As the name suggests, modeling is about finding a model, that is, a simplified representation of your data. In turn, we can view the model as an underlying process that generated your data in the first place, plus some noise. From that point of view, the data you see was generated by the model - and we can say that some of the points you see are less likely to have been generated by your model than others.

For example, if you build a linear regression model, points far away from the regression line are less likely to have been generated by the model. That's what people mean when they talk about 'residuals' in normal statistical parlance. It's also called the likelihood of the data.

Data points that have low likelihood, according to the model you've created, are anomalies or outliers. From a model-building point of view, they are the same thing.

Colloquially, people use the term 'outlier' to mean "something I should remove from the dataset so that it doesn't skew my model I'm building", usually because they have a hunch that there is something wrong with that data and the model they want to build shouldn't need to account for it. An outlier is often considered to being a hinderance to building a model that describes the data overall - simply because the model will ALSO try to explain the outlier, which is not what the practitioner wants.

On the other hand, you can use the fact that a model also assigns a likelihood to each data point to your advantage - might build a model that describes a simpler trend in the data, and then actively look for existing or new values that have very low likelihood. These are what people mean when they say 'anomalies'. If your goal is to detect anomalies, especially in new data, this is a great thing. One person's outlier is another person's anomaly!

Anomaly refers to the patterns in data that do not conform to expected behavior where as Outlier is an observation which deviates from other observations.