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Difference between Global and Contextual outlier

Contextual outliers

Global Outlier

  • Datapoint that deviates significantly from the majority of the data.
  • Global outliers are often detected using statistical methods like the z-score or the interquartile range (IQR) and are based on the distribution of the entire dataset.
  • It can significantly skew summary statistics and have a more pronounced impact on overall data analysis.

Let’s take an example to understand the difference better.

Suppose you have a dataset of quiz scores for a class, and most students score between 70 and 75. If there is a student who scores 95 on one of the exams, this score is a global outlier because it is unusually high compared to the overall range of scores in the dataset.

Python3




import pandas as pd
import matplotlib.pyplot as plt
 
# Sample Dataset
data = {'Student' : ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank', 'Grace'],
        'Math_Score' : [70, 75, 72, 70, 95, 73, 71]
        }
 
# Outlier
contextual_outlier_index = 4
 
# Visualize the data
plt.plot(data['Student'],data['Math_Score'], marker="o")
plt.plot(data['Student'][contextual_outlier_index], data['Math_Score'][contextual_outlier_index],  color = 'red', marker='*', label = "Outlier")
 
plt.xlabel('Name of Students')
plt.ylabel('Marks scored in Mathematics')
plt.title('Class Score in Quiz 6')
plt.legend()
plt.grid()
plt.show()
 






                        









													
													
												

												
												

													
													
Output: 

													
													
												

												
												

													
													
Scores of the class in Quiz 6, where the score of Eve is an Global Outlier

													
													
												

												
												

													
													
Contextual outlier

													
													
												

												
												

													
													
    

  • Datapoints that deviates from the norm when specific conditions or context is taken into account.
    • 

  • These are detected by first defining the relevant context or condition within which they are outliers. Then, outlier detection techniques are applied within that context to identify data points that deviate significantly from the norm within that subset.
    • 

  • It provides valuable insight when analyzing under certain conditions. 
    • 


													
													
												

												
												

													
													
Example:

													
													
												

												
												

													
													




Python3















                                    




                                    

                                    




                                    




                                    

                                    

                                    
                                




















import pandas as pd


import random


import numpy as np


from datetime import datetime, timedelta


import matplotlib.pyplot as plt


 


# Generate random data for the dataset


random_data = {


    'Date': [datetime(2023, 1, 1) + timedelta(days=i) for i in range(30)],


    'Temperature': np.sort([round(random.uniform(-10.0, 40.0), 1) for _ in range(30)]


                           )


}


 


# Create the DataFrame


data = pd.DataFrame(random_data)


 


# Mark temperature values greater than 35 as contextual outliers


contextual_outlier_index = data['Temperature'] > 30


 


# Format date to show only date and month


data['Date'] = data['Date'].dt.strftime('%d-%b')


 


# Visualize the data


plt.plot(data['Date'], data['Temperature'], marker="o")


plt.plot(data['Date'][contextual_outlier_index],


         data['Temperature'][contextual_outlier_index],


         'ro-', label="Contextual Outliers\nIn January temperature is always < 30")


 


plt.xlabel('Date')


plt.ylabel('Temperature')


plt.title('Contextual Outliers')


plt.legend()


# Rotate x-axis labels for better visibility


plt.xticks(rotation=90)  


plt.show()



















                        







                        









													
													
												

												
												

													
													
Output: 

													
													
												

												
												

													
													
Contextual Outliers

													
													
												

												
												

													
													
Detecting contextual outliers typically involves defining the relevant conditions or contexts and then applying outlier detection techniques within those specific subsets of the data. The choice of contextual factors and the methods for identifying and handling contextual outliers will depend on the nature of the data and the goals of the analysis.

													
													
												

												
												

													
													

													
													
												

												
												

													
													

													
													
												

												
												

													
													

													
													
												

												
											

											
											

												
Contextual Outliers

												
												

													
													
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.

													
													
												

												
											

										


										
										

											
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														What are outliers?

													

														Outliers are basically data points in the dataset that significantly differ from the majority of the data. For example, if the majority of the data is lying in the range of 1–50 and one or two points are lying at 125–150, then these points are termed outliers.  These are the values that are exceptionally high or low in comparison to the other data points, which leads to distortions in the overall statistical analysis and interpretation of the data.  It cannot be termed noise or error....
													

												
											

											
											

												
													

														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....
													

												
											

											
											

												
													

														Difference between Global and Contextual outlier

													

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												Contextual outliers