Contents
What is contextual outlier?
Formally, contextual outlier or conditional anomaly is defined as an object with behavior deviating from other objects with similar contextual information [13, 9, 33, 34]. Usually, contextual attributes are used to define the contexts, and objects sharing similar contexts with an ob- ject form its reference group.
What is a global outlier?
Type 1: Global Outliers (also called “Point Anomalies”) A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found (similar to how “global variables” in a computer program can be accessed by any function in the program).
What is an outlier and types of outliers?
Outlier is a data object that deviates significantly from the rest of the data objects and behaves in a different manner. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution errors. An outlier cannot be termed as a noise or error.
What are the different types of global outliers?
Type 1: Global Outliers (also called “Point Anomalies”): A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found (similar to how “global variables” in a computer program can be accessed by any function in the program). Global Anomaly: Type 2: Contextual (Conditional) Outliers:
Which is an example of a contextual outlier?
In a given data set, a data object is a contextual outlier if it deviates significantly with respect to a specific context of the object. Contextual outliers are also known as conditional outliers because they are conditional on the selected context.
When is same value not considered an outlier?
Note that this means that same value may not be considered an outlier if it occurred in a different context. If we limit our discussion to time series data, the “context” is almost always temporal, because time series data are records of a specific quantity over time.
What makes a data point an outlier in statistics?
In statistics and data science, there are three generally accepted categories which all outliers fall into: A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found (similar to how “global variables” in a computer program can be accessed by any function in the program).