Merge pull request #214741 from mrbullwinkle/mrb_10_17_2022_overview

[Cognitive Services] [Anomaly Detector] Primary overview update

Author: prmerger-automator[bot]

articles/cognitive-services/Anomaly-Detector/overview.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: anomaly-detector
 ms.topic: overview
-ms.date: 07/06/2022
+ms.date: 10/17/2022
 ms.author: mbullwin
 keywords: anomaly detection, machine learning, algorithms
 ms.custom: cog-serv-seo-aug-2020
@@ -18,7 +18,6 @@ ms.custom: cog-serv-seo-aug-2020
 
 Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little ML knowledge, either batch validation or real-time inference.
 
-
 This documentation contains the following types of articles:
 * The [quickstarts](./Quickstarts/client-libraries.md) are step-by-step instructions that let you make calls to the service and get results in a short period of time. 
 * The [how-to guides](./how-to/identify-anomalies.md) contain instructions for using the service in more specific or customized ways.
@@ -31,15 +30,14 @@ With the Anomaly Detector, you can either detect anomalies in one variable using
 
 |Feature  |Description  |
 |---------|---------|
-|Univariate Anomaly Detector | Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern. |
-|Multivariate Anomaly Detector| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is Graph attention network.|
+|Univariate Anomaly Detector | Detect anomalies in one variable, like revenue, cost, etc. The model is selected automatically based on your data pattern. |
+|Multivariate Anomaly Detector| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is a Graph Attention Network (GAT).|
 
 ### When to use **Univariate Anomaly Detector** v.s. **Multivariate Anomaly Detector**
 
 If your goal is to detect anomalies out of a normal pattern on each individual time series purely based on their own historical data, use univariate anomaly detection APIs. For example, you want to detect daily revenue anomalies based on revenue data itself, or you want to detect a CPU spike purely based on CPU data.
 
-If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs. Particularly, when any individual time series won't tell you much, and you have to look at all signals (a group of time series) holistically to determine a system level issue. For example, you have an expensive physical asset like aircraft, equipment on an oil rig, or a satellite. Each of these assets has tens or hundreds of different types of sensors. You would have to look at all those time series signals from those sensors to decide whether there is system level issue.
-
+If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs. Particularly, when any individual time series won't tell you much, and you have to look at all signals (a group of time series) holistically to determine a system level issue. For example, you have an expensive physical asset like aircraft, equipment on an oil rig, or a satellite. Each of these assets has tens or hundreds of different types of sensors. You would have to look at all those time series signals from those sensors to decide whether there is a system level issue.
 
 ## Demo