Univariate Anomaly Detection - Historical Data and Latest Data

What is univariate anomaly detection?

The univariate anomaly detection is the simplest form of anomaly detection where the machine learning algorithm needs to learn only a single feature space. This means that there is a single feature value associated with the timestamp. For example, to determine the anomalies in the incomes of people living in the United States we would need only one feature—the income itself.

Dependencies

To work with this chapter and run the code snippets on your local machine, you would need the following package to be installed:

• azure-ai-anomalydetector

To know how to install the required package, please visit the Appendix section.

Implementation

To properly cover univariate anomaly detection, we’ll perform three types of detection:

• Detecting the anomalies in the historical data set: We’ll use time-series data ...