Anomaly detection in cross-sectional data: A beginner’s guide

In today's data-driven world, a single anomaly can distinguish between a million-dollar insight and a costly misstep. However, spotting these outliers is akin to finding a needle in a multidimensional haystack regarding cross-sectional data.

Cross-sectional data is a snapshot of a population at a specific point in time. Unlike time-series data, which tracks changes over time, cross-sectional data provides a static view across multiple variables or dimensions. Think of it as a comprehensive health check-up: it captures various metrics simultaneously, offering a holistic view of the subject's condition at that moment.

What is anomaly detection in cross-sectional data?

Anomaly detection in cross-sectional data refers to identifying outliers or unusual patterns in datasets collected simultaneously across multiple subjects or units (e.g., different companies, products, or individuals). Cross-sectional data provides a snapshot, offering insights into characteristics or behaviors during a specific timeframe.

Anomaly detection in cross-sectional data involves identifying observations that deviate significantly from the norm across multiple dimensions. These outliers can represent errors, fraud, or unique insights that warrant further investigation.

What are anomalies in cross-sectional data? Well, anomaly detection can be broadly categorized into

• Point Anomalies: These are individual data points that differ substantially from the other entities in the dataset. For instance, a customer with an unusually high purchase value compared to others in a retail dataset.
• Contextual Anomalies: These anomalies occur when a data point is only abnormal in relation to a specific context or subgroup. For example, a salary that seems appropriate in a general dataset may be anomalous when considering only individuals within a particular profession..
• Collective Anomalies: These involve groups of data points that collectively differ from the overall distribution. While individual points may not seem unusual, their collective behavior indicates an anomaly.

Anomaly detection in cross-sectional data helps organizations flag potential issues early, such as fraud, manufacturing defects, or operational inefficiencies, improving decision-making and risk management. Cross-sectional data consists of observations collected from various entities at a specific point in time without considering temporal aspects. For instance, a dataset might include income, age, and education level data for a group of individuals, but all observations pertain to a single time period.

How to detect anomalies in cross-sectional data?

Interestingly, there are multiple proven techniques and different approaches available presently through which data engineers can quickly detect anomalies in cross-sectional data. Let’s deep dive into some proven techniques used for anomaly detection in cross-sectional data:

1. Statistical methods

• Z-Scores: Z-scores measure the number of standard deviations a data point is from the mean. A high absolute Z-score may indicate an anomaly.
• Percentile-Based Methods: These methods flag points that fall above or below certain percentiles (e.g., the top or bottom 1%) in the distribution.
• Boxplots: Visualizing data with boxplots can reveal outliers beyond the interquartile range.

2. Machine Learning algorithms

• Clustering Algorithms: Techniques like k-means or DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can group data points based on similarity. Points that do not belong to any cluster or are in sparsely populated clusters may be anomalies.
• Isolation Forests: This tree-based method isolates data points by randomly selecting features and splitting them. Anomalies are easier to isolate and thus require fewer splits.
• Support Vector Machines (SVMs): Specifically, one-class SVMs can be used to identify the boundary that separates normal data points from outliers.

3. Distance-based methods

• K-Nearest Neighbors (KNN): In this approach, a point is considered anomalous if it has few neighboring points within a defined radius.
• Mahalanobis Distance: This method measures the distance between a data point and the mean of the dataset, taking into account the covariance of the data. Higher distances suggest anomalies.

4. Probabilistic models

• Gaussian Mixture Models (GMM): These models assume the data is generated from a mixture of several Gaussian distributions. Anomalies are points with a low likelihood of belonging to any distribution.
• Bayesian Networks: These models calculate the probability of each observation based on a set of probabilistic dependencies. Low-probability points are flagged as anomalies.

Major challenges in cross-sectional anomaly detection

The journey of anomaly detection, particularly in cross-sectional data, reflects multiple severe challenges to the data engineers. Here is a closer look at the key challenges:

• Curse of dimensionality

When dealing with high-dimensional data, traditional distance-based or clustering techniques may struggle to differentiate between normal and anomalous points effectively. In high dimensions, the distance between points becomes less meaningful, and models may find it difficult to isolate anomalies.

Data engineers often rely on dimensionality reduction techniques, primarily Principal Component Analysis (PCA), to overcome the curse of dimensionality. However, this approach is prone to the major disadvantage of removing important information.

• Imbalanced data

Anomalies can be explained as events wherein the datasets used are completely imbalanced. This is the biggest issue, as most data learning models feed on the majority class (normal data points) to provide near-to-accurate performance.

With imbalanced datasets, they fail to detect the minority class (anomalies) early. As a result, data experts have to deal with poor recall and precision for anomalies unless proven techniques like oversampling, undersampling, or specialized algorithms are deployed.

• Feature selection and representation

Selection of the right feature set is very important when performing anomaly detection because substandard (read poor) features can mask the detection of anomalies. Particularly in cross-sectional data, when data engineers struggle to define the relevance of features for the algorithm, they have to deal with more false positives.

• Lack of labels for supervised learning

Anomalies without labels make the adoption journey of supervised learning approach quite difficult to apply. In cross-sectional dataset, this problem multifolds as cross-sectional data demands manual identification and labeling outliers - the entire process is very time-consuming and subjective for data engineers.

To navigate this challenge, many methods of anomalies detection rely on unsupervised or semi-supervised learning without labeled data.

• Contextual and collective anomalies

An early detection of contextual and collective anomalies is a major roadblocker for many data engineers when utilizing cross-sectional data. Generally, data point remains anomalous in a particular scenario or a specific dataset subgroup, making detection of contextual and collective anomalies extremely difficult even by adopting global techniques.

Similarly, when dealing with cross-sectional data, data engineers need to deploy more sophisticated approaches, such as clustering or temporal analysis, to detect collective anomalies, which are referred to as specific groups of points (collectively building an abnormal pattern).

• Scalability

Considering large-scale structures, the administration of cross-sectional datasets implies high computational complexity to execute the full range power of both progressive machine learning models or pertinent distance standards, namely k-Nearest Neighbors (KNN) or Isolation forest. It is an issue of scalability because as the size of the dataset grows, the time and computational resources needed for data anomalies to be detected rise steeply.

• False positives

In cross-sectional data, anomalies mean many false positives, as standard points are detected as outliers. This is similar to high-dimensional datasets, wherein the presence of irrelevant features turns standard points into anomalous identities. To deal with this, data engineers

• Interpreting results

Detection of the anomalous points at times seems difficult to rationalize. When anomalies are detected, data specialists struggle to identify why a point is considered anomalous because the points are in a multi-dimensional dataset. Even deploying black-box machine learning models like neural networks or complex tree-based methods may not give a clear understanding of these anomalies. As a result, the detected anomalies hamper the overall performance of cross-sectional datasets.

Best practices to avoid anomaly detection in cross-sectional data 

To navigate complexities of anomaly detection in cross-sectional data, data engineering experts usually rely on the implementation of several techniques and proven practices. These approaches are result-oriented, and ensure accurate detection of anomalies with visibly reduced false positives, and accurate value-driven actionable insights. Below are some key best practices to avoid anomaly detection in cross-sectional data:

1. Understand the data and define anomalies

The first step, especially before applying any detection techniques, is to identify the dataset's structure and background. Propose logic to classify an anomaly regarding the specific field of study.

For example, outliers in financial data could indicate a fraudulent transaction, while outliers in healthcare data might indicate a rare disease. A clear understanding of the domain under consideration allows one to set definite mental points on the spectrum of normal and anomalous.

2. Use robust preprocessing

Cleaning the data is the first process of preprocessing. It assists in eliminating noisy and missing values or features that might be irrelevant data. Noisy data can greatly contribute to increasing the number of false positives.

The next major step in the journey is handling data so that features can be comparable, which can be done by normalization/standardization. Min-max scaling or Z-score normalization is used to control data that might have high to low values.

3. Leverage dimensionality reduction

When working with high-dimensional cross-sectional data, it is essential to employ techniques such as PCA or t-SNE for maximum dimensionality reduction while providing relevant data. Adopting these techniques also enables the discovery of a pattern and reduces computational processing. Interestingly, dimensionality reduction also helps data engineers reduce the “Curse of Dimensionality,” as traditional distance-based algorithms fail to find good isolating boundaries.

4. Use multiple anomaly detection methods

It has been witnessed that different algorithms perform in different ways under divergent conditions, which indicates that when data engineers combine various methods together, they experience an increase in robustness. For example:

• Statistical methods which detect outliers-based probability distribution (e.g., Z-scores).
• Machine Learning algorithms wherein data engineers leverage popular models such as Isolation Forests, One-Class SVM, or Autoencoders to handle complex data patterns while detecting non-linear relationships.
• Clustering approaches that use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to identify collective anomalies.

5. Employ ensemble methods

Ensemble approaches primarily involve various models of anomaly detection to attain enhanced performance and minimal false positives. For example: when data engineers combine results extracted from statistical methods with machine learning algorithms, they can instantly identify global and local anomalies.

6. Handle imbalanced data

When we refer to real-world applications, anomalies account for a very minor fraction of cross-sectional datasets. Data engineering teams use techniques like oversampling minority classes (anomalies) or undersampling the majority class to create a right balance between the dataset and enhance the detection accuracy ratio. Additionally, many experts rely on techniques like synthetic data generation (e.g., SMOTE) to identify anomalies in highly imbalanced datasets.

7. Evaluate using appropriate metrics

Traditional accuracy metrics can be misleading for anomaly detection tasks due to the imbalance between normal and anomalous data points. Focus on metrics like:

• Precision and Recall: To measure how well the model identifies true anomalies.
• F1-Score: The harmonic mean of precision and recall.
• ROC-AUC: To assess the model’s ability to distinguish between normal and anomalous points.

Avoid relying solely on accuracy, which may mask the poor performance of the minority class (anomalies).

8. Use contextual features for better insights

When anomalies are context-dependent because one data point often seems normal in one particular context, but data engineers identify anomalies in another subset of data, contextual features are deployed. Contextual features play a pivotal role in refining the detection process and minimizing false positives.

For example, an individual’s salary might not be an anomaly in the entire dataset but could be considered anomalous when contextualized by job role, experience, or industry.

By adhering to these best practices, organizations can significantly enhance their ability to detect and interpret anomalies in cross-sectional data. This improves data quality and decision-making and uncovers valuable insights that may have otherwise remained hidden in the complexity of multidimensional datasets.

Remember, the key to successful anomaly detection lies in sophisticated algorithms and a holistic approach that combines statistical rigor, domain expertise, and continuous improvement.