What is anomaly detection in time series data?

What is anomaly detection in time series data?

Anomaly detection in time series data involves identifying unusual patterns or outliers that deviate from expected behavior.

Why is it important to detect anomalies in time series data?

Detecting anomalies is crucial for identifying potential issues, such as fraud, system failures, or unexpected changes in trends.

What are some common methods used for anomaly detection?

Common methods include statistical tests, machine learning algorithms, and time series decomposition techniques.

Can you give an example of an application of anomaly detection?

Sure! Anomaly detection is used in finance to detect fraudulent transactions by identifying unusual spending patterns.