Contextual outliers

These are the outlier that is identified within a specific context or condition. In other words, the data point which may not be considered an outlier normally, but when we take into account the domain knowledge they become outliers. They are often analyzed in situations where the data varies based on different factors or attributes.

Here are some examples of contextual outliers:

• Temperature Anomaly: Imagine you’re monitoring the daily temperature in a city for a year. Most of the time, the temperature falls within a certain range. However, if there’s a day in the summer when the temperature suddenly drops significantly, that day’s temperature would be a contextual outlier because it deviates from the expected temperature for that season.
• Stock Market Fluctuations: In the stock market, the daily price of a stable stock may not be considered an outlier if it remains within a certain range. However, if a stock experiences a sudden and sharp price increase or decrease during a specific news event or earnings report, that day’s price change might be considered a contextual outlier within the context of that event.
• Website Traffic: A website usually has a fairly consistent number of daily visitors. However, during a major event, such as a product launch or a viral social media post, the website’s daily traffic might spike significantly. The days with high traffic would be contextual outliers within the context of normal daily traffic.
• Retail Sales: In a retail store, certain products may have steady sales figures throughout the year. However, during a holiday season or a special promotion, the sales of those products may increase substantially, making those sales periods contextual outliers in terms of sales volume.
• Hospital Admissions: In a hospital, the number of daily patient admissions typically follows a pattern. However, during a disease outbreak or a natural disaster, the hospital may experience a sudden surge in patient admissions, making those days contextual outliers in terms of hospital utilization.

Identifying and analyzing contextual outliers is important in various fields, such as statistics, finance, and anomaly detection, as they can provide valuable insights into unique events or conditions that affect the data.

Understanding contextual outliers is essential across various fields, including statistics, finance, and anomaly detection, as they offer valuable insights into unique events or conditions that impact the data. By identifying and analyzing these outliers, we gain a deeper understanding of the nuances within our datasets, enabling us to make more informed decisions and draw meaningful conclusions within specific contexts.

This article explores the fascinating world of contextual outliers, shedding light on their significance and how they differ from global outliers. We’ll illustrate the concept with real-world examples, demonstrating how contextual outliers emerge when certain conditions or events come into play.