---
description: >-
  Batch detection analyses groups of data to identify anomalies by comparing
  data points against expected values, flagging outliers.
---

# Batch Detection

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

**Batch anomaly detection** is used to identify anomalies in data that is processed in groups or batches rather than in real-time. This method is applied to a collected set of data, typically over a specific period, to analyze and detect any outliers or unusual patterns.

In IoT/OT environments, batch anomaly detection is useful for analyzing historical data to identify trends, seasonal variations, or anomalies that may not be immediately apparent in real-time. It's often used for periodic reporting, trend analysis, and retrospective investigations of system behavior.

<figure><img src="blob:https://app.gitbook.com/8be2916b-b8e8-4038-8233-082d6936daba" alt=""><figcaption><p>Output of Batch Detection Anomoly</p></figcaption></figure>

### How does it work?

Identifies **anomalies** across the **entire** time series data.

Sends the most recent **1000** data points to the AlphaX intelligence engine to build a model, then analyses **each** data point based on that model.

#### Parameters <a href="#parameters.1" id="parameters.1"></a>

<table><thead><tr><th width="232">Feature</th><th>Description</th></tr></thead><tbody><tr><td>Sensitivity</td><td>A numerical value (0% - 99%) that adjusts the tolerance of Anomaly Detection. Lowering the sensitivity leads to fewer anomalies detected.</td></tr><tr><td>Max Anomaly Ratio</td><td>A numerical value (0% - 49%) that indicates the maximum ratio of anomalies to be detected from a time series. For example, if it's set to default 0.25 (25%), for a time series with 100 points, the maximum number points identified as an anomaly would be 25.</td></tr></tbody></table>

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

<table><thead><tr><th width="251">Feature</th><th>Description</th></tr></thead><tbody><tr><td>Sensor</td><td>The actual measured value from the sensor or system</td></tr><tr><td>Anomaly</td><td>Boolean: If the data point is Identified as an anomaly</td></tr><tr><td>Expected</td><td>The expected or predicted values of the data points in the time series</td></tr><tr><td>Lower Margin</td><td>Lower margin of each date point, used to calculate Lower Boundary.</td></tr><tr><td>Upper Margin</td><td>Upper margin of each date point, used to calculate Upper Boundary.</td></tr></tbody></table>

#### Boundaries

**Boundaries** in batch mode anomaly detection define the expected limits for data values within a batch. These boundaries help the user to visualise whether a data point is normal or anomalous but are not absolute.

**High** (Lower Boundary): Calculated as the expected value plus the average of the current and previous 2 upper margins.

**Low** (Upper Boundary): Calculated as the expected value minus the average of the current and previous 2 lower margins.\
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