How to get started with anomaly detection algorithms in 5 minutes
Anomaly detection has quickly moved out of computer science theory into practical everyday use by data scientists. Now, it’s an essential part of data cleaning and KPI reviews for many businesses across the world. Overall, it greatly increases the accuracy of predictive models and can help businesses identify and respond to anomalies quickly.
To help you get started with this dense subject today, we’ll explore a 5-minute crash course on what anomaly detection is, why it’s used, and some basic algorithms.
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What is anomaly detection?#
Anomaly detection is a mathematical process used by data scientists to detect abnormalities within supervised and unsupervised numerical data based on how different a data point is from its surrounding data points or from the standard deviation.
There are many different anomaly detection techniques, sometimes called outlier detection algorithms, that each have different criteria for outlier detection and are therefore used for different use cases. Anomaly detection is used across all the major data science technologies such as, Python and Scikit-learn (SKlearn).
All forms of anomaly detection rely on first building an understanding of standard results, or normal instances, using time series data.
Time series data is essentially a collection of values of the same variable over a period of time. This does not typically mean constant or the same but rather changing in an expected way. Each technique uses different estimator criteria to form the benchmark.
Example: Ice cream sales#
For example, an ice cream store may record a drop in sales during the winter months and a peak in sales during the summer months. This trend is consistent year after year and is therefore used as the expected standard.
Even though a drop in sales is a notable change compared to summer month sales, it’s not an anomaly. However, the system would flag an anomaly if ice cream sales suddenly spiked 40% above normal during a winter month because that’s outside of our expected sales behavior. This would allow the ice cream store to analyze why that anomaly occurred and make informed business decisions on how to increase sales again in the future.
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Why is anomaly detection important?#
Anomaly detection is an essential part of every modern machine learning technique. It helps you build more adaptive regression systems, clean defects from classifier system training data, and remove anomalous data from supervised learning programs. This mathematical approach is especially useful for big data and data mining applications because it’s nearly impossible for the human eye to notice outliers in data visualizations that feature several thousand data points.
Due to its diverse number of use cases, businesses from different sectors have all been implementing anomaly detection in their data strategies. For example, many companies have opted to use anomaly detection methodsto track their key performance indicators (KPIs). This allows them to notice anomalous trends quicker on paper and be more agile in shifting real-world markets.
Anomaly detection has also been adopted by cybersecurity experts for advanced artificial intelligence-powered fraud detection and intrusion detection systems. These systems use advanced data analysis techniques to track and flag suspicious user behavior in real-time.
Density-based techniques#
Density-based techniques encompass common techniques like K-Nearest Neighbor (KNN), Local Outlier Factor (LOF), Isolation Forests (similar to decision trees), and more. These techniques can be used for regression or classification systems.
Each of these algorithms generates an expected behavior by following the line of highest data point density. Any points that fall a statistically significant amount outside of these dense zones are flagged as an anomaly. Most of these techniques rely on distance between points, meaning it’s essential to normalize the units and scale across the dataset to ensure accurate results.
For example, in a KNN system data points are weighted by a value of 1/k, in which k is the distance to the data point’s nearest neighbor. This means data points that are closer together are weighted heavily and therefore influence what’s standard more than distant data points. The system then flags outliers by looking at points that have a low 1/k value.
Use Case
You have normalized, unlabeled data that you want to scan for anomalies but you’re not interested in algorithms with complex computations.
One-class support vector machine#
The one-class support vector machine (one-class SVM) algorithm is a supervised learning model that produces a robust prediction model. It’s mainly used for classification. The system uses a training set of examples, each marked as being part of one of two categories. The system then creates criteria with which to sort new examples into each category. The algorithm maps examples to points in space to maximize the differentiation between both categories.
The system flags an outlier if it falls too far out of either category’s space. If you don’t have labeled data, you can use an unsupervised learning approach that looks for clustering among examples to define categories.
Use Case
You have data that should mostly fit within two expected categories and want to find which data points lay outside of either category.
K-means clustering anomaly detection algorithm#
The K-means clustering algorithm is a classification algorithm similar to KNN approaches because it relies on the closeness of each data point to other nearby points and is similar to SVM because it primarily focuses on classification into different categories.
Each data point is split into categories based on its features. Each category has a central point, or centroid, that serves as a prototype for all other data points within the cluster. Other points are then compared against these prototypes to determine their k-mean value, which essentially acts as a metric of difference between the prototype and the current data point. Higher k-mean data points are mapped closer to the prototype, creating a cluster.
K-means clustering can detect anomalies by flagging points that do not closely align with any of the established categories.
Use Case
You have unlabeled data composed of many different types of data that you want to organize by likeness to learned prototypes.
Deep learning approaches to anomaly detection#
While classical algorithms like KNN, LOF, and One-Class SVM are great starting points, many modern anomaly detection systems now rely on deep learning. These methods can capture complex, non-linear patterns in data that traditional models often miss.
Autoencoders and Variational Autoencoders (VAEs)#
Autoencoders are neural networks that learn to reconstruct input data. If an input is very different from what the model has learned, its reconstruction error will be high, signaling an anomaly.
Use case: Detecting fraudulent transactions or sensor malfunctions in IoT devices.
VAE advantage: Variational autoencoders add a probabilistic element, which helps detect subtle deviations and handle noisy data better.
GAN-based anomaly detection#
Generative Adversarial Networks (GANs) are another deep learning approach. By learning the distribution of “normal” data, GANs can flag samples that deviate from that distribution. Techniques like AnoGAN are widely used in domains like image and video anomaly detection.
Transformer-based methods#
Transformers and attention-based models are now common in time-series anomaly detection. Their ability to capture long-term dependencies makes them particularly powerful for detecting rare events across large, sequential datasets — for example, server outages or irregular user behavior over time.
Time-series and streaming anomaly detection#
Many real-world anomalies occur in time-dependent data — think of stock prices, sensor logs, or server metrics. Detecting these requires specialized approaches.
Forecasting residual anomalies#
One simple but powerful method is to train a model (like ARIMA, Prophet, or an LSTM) to forecast future values. If the actual data deviates significantly from the prediction, it’s flagged as an anomaly.
Change-point detection#
Change-point algorithms detect when the underlying statistical properties of a time series shift, a key signal that something unusual is happening. These algorithms are essential in monitoring systems and cybersecurity.
Streaming and online detection#
Data often arrives in real time in production environments. Algorithms like streaming KNN, online Isolation Forest, and adaptive windowing allow continuous anomaly detection without retraining from scratch.
Graph and relational anomaly detection#
Anomalies aren’t always individual points — they can also appear as unusual relationships in networks or graphs. Modern systems often use graph neural networks (GNNs) to detect these irregularities.
Node anomalies: A user node in a social graph suddenly connects to hundreds of new accounts.
Edge anomalies: Unusual communication patterns between devices in a network.
Subgraph anomalies: Fraud rings or coordinated malicious activity.
Graph-based detection is becoming increasingly important in cybersecurity, recommendation systems, and fraud analysis.
Evaluating anomaly detection models#
Anomaly detection is fundamentally different from standard classification — anomalies are rare and labels are often scarce. That means evaluation requires extra care.
Metrics to use#
Precision and recall: Precision is critical when false positives are costly; recall matters when missing an anomaly is riskier.
F1-score: A balance between precision and recall.
ROC-AUC and PR-AUC: Useful for comparing models, especially in highly imbalanced datasets.
Threshold selection#
Choosing the right threshold for anomaly scores is crucial. Techniques like the elbow method, Youden’s J statistic, or cross-validation with known anomalies can help set thresholds that balance sensitivity and specificity.
Explainability and interpretability#
In production systems, detecting an anomaly is only half the job — you also need to explain why it was flagged. This builds trust and speeds up response time.
Feature attribution: Use methods like SHAP or LIME to show which features contributed most to the anomaly score.
Rule extraction: Combine statistical models with rule-based systems for clear, human-readable explanations.
Visual diagnostics: Plot anomaly scores over time or show feature distributions for flagged instances.
Explainability is especially critical in regulated industries like finance and healthcare.
Handling concept drift and evolving data#
Real-world data changes over time — what’s “normal” today might be anomalous tomorrow. This phenomenon, known as concept drift, requires models to adapt.
Windowing approaches: Re-train models periodically on a moving window of the most recent data.
Online learning: Update the model incrementally as new data arrives.
Feedback loops: Incorporate feedback from analysts or end users to refine anomaly definitions over time.
Ignoring drift leads to rising false positives, stale models, and missed anomalies.
Hybrid and ensemble methods#
State-of-the-art anomaly detection systems often combine multiple algorithms to achieve better performance. For example:
Statistical + ML: Use statistical methods for quick detection and machine learning for deeper analysis.
Multiple models: Combine Isolation Forest, autoencoders, and clustering-based methods to reduce false positives.
Cascaded systems: Deploy lightweight models for real-time monitoring and heavier models for offline batch analysis.
Ensembles are especially valuable in high-stakes domains like fraud detection, cybersecurity, and industrial monitoring.
Tooling and libraries#
The ecosystem around anomaly detection has grown significantly. Beyond scikit-learn, you should be aware of dedicated libraries and cloud services:
PyOD: A comprehensive Python library with 40+ anomaly detection algorithms.
Anomalib: Specialized for deep learning–based anomaly detection, including VAEs and GANs.
River: Focused on streaming and online anomaly detection.
Cloud platforms: AWS Lookout for Metrics, Azure Anomaly Detector, and Google Vertex AI Anomaly Detection offer managed solutions.
Including a short section on tools helps readers quickly experiment and build prototypes without reinventing the wheel.
Real-world deployment best practices#
To bridge the gap between theory and production, consider adding a practical section on how anomaly detection systems are deployed at scale:
Pipeline design: Ingest → detect → alert → explain → feedback → retrain.
Alert management: Use severity scores, batching, and correlation to avoid alert fatigue.
Monitoring: Continuously track precision, recall, and drift metrics in production.
Feedback loops: Allow humans to label false positives and feed that data back into the model.
This section turns a blog from “tutorial” to “real-world guide,” which developers increasingly expect in 2025.
Algorithms to learn next#
There are many other advanced algorithms out there, each with its own advantages. Some specialize in unsupervised anomaly detection and others can measure multivariate data sets. As you continue your anomaly detection journey, check out these intermediate algorithms.
- Gaussian: An alternative version of the K-means algorithm that uses Gaussian distribution versus standard deviation.
- Bayesian: An alternative algorithm that leverages the Bayesian understanding of probability to detect anomalies.
- Autoencoders: A form of neural network that bounces expectations between input and output. The system uses the input to create encoded rules for expected output and vice versa. Any values that fall outside these recurring layers of analysis are flagged as anomalous. This learning algorithm is mainly used for anomaly detection problems of dimensionality.
To help you continue to develop your anomaly detection skills, Educative has created the course Simple Anomaly Detection using SQL. This brief course is the ideal crash course to get you hands-on with anomaly detection in just a few short lessons. With step-by-step directions through each step of the process, this course will walk you through building your first anomaly detection system using SQL. Complete this course for free using our 1-week free trial.
Happy learning!
