Global anomalies and local anomalies are two types of outliers in data, distinguished by the scope of their deviation. A global anomaly is a data point that significantly differs from the entire dataset. For example, in a dataset of daily temperatures ranging from 10°C to 30°C, a reading of 100°C would be a global anomaly because it’s far outside the expected range. These anomalies are straightforward to detect using basic statistical methods like standard deviation thresholds or z-scores, as they don’t require context beyond the dataset’s overall distribution.
Local anomalies, however, are deviations within a specific subset or context of the data. For instance, a temperature of 15°C might seem normal globally, but if it occurs in a tropical region where temperatures consistently stay near 25°C, it becomes a local anomaly. Detecting these requires analyzing relationships within smaller clusters or neighborhoods of data points. Techniques like clustering (e.g., DBSCAN) or density-based methods (e.g., Local Outlier Factor) are often used, as they compare a point’s behavior to its immediate surroundings rather than the entire dataset.
The key practical difference for developers lies in how they approach detection. Global anomalies can often be identified with simple thresholds or basic statistical models, making them easier to implement. Local anomalies, however, demand more sophisticated algorithms and domain knowledge to define meaningful contexts or neighborhoods. For example, in fraud detection, a sudden $1M transaction (global anomaly) might be flagged instantly, while a series of smaller, unusual purchases in a specific user’s history (local anomaly) requires analyzing patterns within that user’s behavior. Choosing the right method depends on understanding whether the anomaly is context-independent or tied to a subset of the data.