What is a key characteristic of Anomaly detection?

Anomaly detection is primarily focused on identifying outliers or unusual data points within a dataset, which sets it apart as a valuable tool in various fields like finance, cybersecurity, and quality control. These outliers may represent critical incidents, errors, or novel patterns that require further investigation. By identifying these anomalies, systems can trigger alerts or prompt deeper analyses, leading to better insights and decision-making.

The other options highlight misconceptions about anomaly detection. While it can analyze numerical data, it is not restricted to just that type; it can also handle categorical data depending on the methods used. The assertion that it is limited to financial data is incorrect since anomaly detection techniques can be applied across different domains. Lastly, while some anomaly detection methods do not require historical data, many effective techniques often use historical datasets to establish normal behavior patterns, making context important for accurate outlier identification.