# Streaming Detection

### **What is it for?**

**Streaming anomaly detection** is used to identify unusual patterns or outliers in real-time data as it is continuously generated. Unlike traditional methods that analyze historical data, streaming anomaly detection operates on live data streams, making it ideal for monitoring systems that require immediate detection and response to anomalies.

In IoT/OT environments, it's used to detect issues like equipment malfunctions, security breaches, or operational problems as they occur, enabling rapid intervention to prevent potential problems or mitigate their impact.

### How does it work?

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This detection mode is set from the events module and analyses each new data point as it is received. For more detail on setting the streaming anomoly mode refer to the events module. 
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Identifies if **the current point** of the time series data is anomaly.

This sends the most recent **1000** data points to the AlphaX intelligence engine to build a model, then analyzes the **last** data point based on that model.

#### Parameters <a href="#parameters.3" id="parameters.3"></a>

<table><thead><tr><th width="222">Feature</th><th>Description</th></tr></thead><tbody><tr><td>Sensitivity</td><td>A numerical value (0% - 99%) used to control the tolerance of Anomaly Detection. Lower sensitivity results in fewer anomalies being detected. It is recommended to set the sensitivity below 75% to prevent over-sensitivity and reduce false positives.</td></tr><tr><td>Max Anomaly Ratio</td><td>Not used</td></tr></tbody></table>

#### Output <a href="#output.2" id="output.2"></a>

<table><thead><tr><th width="226">Feature</th><th>Description</th></tr></thead><tbody><tr><td>Anomaly</td><td>Boolean: If the <strong>Last</strong> data point is Identified as an anomaly</td></tr></tbody></table>

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