Outlier Detection in Python, Kennedy B., 2025

Outlier Detection in Python, Kennedy B., 2025.
    
   Outliers—values that appear inconsistent with the rest of your data—can be the key to identifying fraud, performing a security audit, spotting hot activity, or just assessing the quality of a dataset. This unique guide introduces the outlier detection tools, techniques, and algorithms you’ll need to find, understand, and respond to the anomalies in your data.
Outlier Detection in Python illustrates the principles and practices of outlier detection with diverse real-world examples including social media, finance, network logs, and other important domains. You’ll explore a comprehensive set of statistical methods and machine learning approaches to identify and interpret the unexpected values in tabular, text, time series, and image data. Along the way, you’ll explore scikit-learn and PyOD, apply key OD algorithms, and add some high value techniques for real world OD scenarios to your toolkit.




Ruptures.
Another type of anomaly we often look for in time-series data is what is known as a rupture (also known as a regime change, level change, change point, or breakpoint). This occurs when the data shows a change, such as a jump to a higher level or a change in volatility but the change is not short-lived; it appears to be the new normal, though it is possible the data will eventually return to the previous levels. An example is seen in figure 17.3, where the level changes between 40 seconds and 80 seconds and then returns to nearly the previous level, making this time period possibly anomalous, or at least noteworthy.

While the regime change itself is likely worth flagging as an outlier (assuming these are rare), we also need to be careful, after the regime change, to compare points to comparable points; that is, we should compare each point to other points within their regime. The point at 120 seconds, which stands out as an outlier, does so only if it is compared to the regime it is within, which covers roughly 80 seconds to 160 seconds. This regime has a different level and different (lower) variance than other periods, making it likely invalid to compare a point during this period to other periods.

Contents.
Part 1.
1 Introducing outlier detection.
2 Simple outlier detection.
3 Machine learning-based outlier detection.
4 The outlier detection process.
Part 2.
5 Outlier detection using scikit-learn.
6 The PyOD library.
7 Additional libraries and algorithms for outlier detection.
Part 3.
8 Evaluating detectors and parameters.
9 Working with specific data types.
10 Handling very large and very small datasets.
11 Synthetic data for outlier detection.
12 Collective outliers.
13 Explainable outlier detection.
14 Ensembles of outlier detectors.
15 Working with outlier detection predictions.
Part 4.
16 Deep learning-based outlier detection.
17 Time-series data.



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