Smart Alert

Smart alerts employ anomaly detection and intelligent alert algorithms based on time series prediction.

Algorithm Principles

Time Series Prediction-Based Anomaly Detection Algorithm

Statistical methods based on data models are the most widely used anomaly detection techniques. The basic principle is: perform statistical modeling on training datasets (usually normal examples). If a data sample does not conform to the trained stochastic model, it is identified as an anomaly sample.

Machine Learning-Based Intelligent Alert Algorithm

Through time series prediction-based anomaly detection algorithms, the alert system can automatically and accurately complete fault detection and alerting tasks, greatly reducing the burdensome manual setting of various performance metric alert thresholds and manual management tasks.

Advantages

No need for complex threshold configuration

Automatically adapts to website performance changes

Reduces false positives through continuous learning

Suitable for websites with variable performance patterns

Configuration Steps

Select the monitoring task to configure alerts for.

Click Create Alert and select Smart Alert.

Configure alert basic information.

Configure learning period and sensitivity settings.

Configure alert notification settings.

Click Save to complete smart alert configuration.

Algorithm Principles#

Time Series Prediction-Based Anomaly Detection Algorithm#

Machine Learning-Based Intelligent Alert Algorithm#

Advantages#

Configuration Steps#

Algorithm Principles

Time Series Prediction-Based Anomaly Detection Algorithm

Machine Learning-Based Intelligent Alert Algorithm

Advantages

Configuration Steps