Time Series Anomaly Detection in Power BI using Cognitive Service and Power Query

I am excited about this blog post, this is based on the New service in Cognitive Service name “Anomaly Detection” which is now in Preview.
I recorded a video about how it works in cognitive service https://youtu.be/7ZOtZDbn6gM. 

However, I am going to talk about how to use it in Power BI. In this post first, a brief introduction to the anomaly detection will be presented, then how it can be used inside Power BI will be discussed.

Anomaly detection

Anomaly detection is an approach in machine learning, that able to detect the rare data points, events or observation in the data that are different from the majority of the data. There are many algorithms that able to be used for this purpose. In Microsoft Cognitive Service they mainly used  Spectral Residual (SR) and Convolutional Neural Network (CNN).  The anomaly Detection in Microsoft is a time-series anomaly detection service. Check the Microsoft research article from here

Time Series Anomaly detection in Microsoft Cognitive Service Cognitive Service 

to have an overview of how it works,
1- Navigate to https://azure.microsoft.com/en-us/services/cognitive-services/

2- under the decision click on the Anomaly detection

3- Click on the Try Anomaly detector to get the Key and endpoint 

in the https://docs.microsoft.com/en-nz/azure/cognitive-services/anomaly-detector/ there is some great articles on how to use Anomaly detection.

to see a live demo of Anomaly detection and how it works you can use the below link


also, I have recorded a video on how to use it in RADACAD channel

Power BI and Time Series Anomaly detection

there is a document in the Microsoft webpage about how to connect to the API of Anomaly detection, which was a big help, but I have to change some part of the code to make it work as I though the API of the anomaly detection change as it is in Preview, so I have to change the M code to make it work, but in future also this one may change as well if the API change to make sure it works need to check the input JSON data. Thanks to Reza Rad to help me in some part of this process 🙂

This the original great blog post that I change it to make work with new API


so what we need


there is a specific format of data that you can use

it should be two columns, one is for the date with the name “timestamp”, the other one is “value”

This is the specific format for the time

in my scenario, it was the daily data, so in the API we have a field name granularity, so the form was daily.


use this dataset for your demo also


Function for calling The API

now we need to create a function to call the API,

need to go to the Power Query Editor

Then in the Query list create a new Query and Blank Query

Then click on the Advanced Editor to paste the below code

The in the new Query Editor, remove everything and paste the code

the above code is here, ( check the image as the format may change while copying from here)

(table as table) => let

apikey = “Your API Code”,
endpoint = “https://westus2.api.cognitive.microsoft.com/anomalydetector/v1.0/timeseries/entire/detect”,
inputTable = Table.TransformColumnTypes(table,{{“timestamp”, type text},{“value1″, type number}}),
jsontext = Text.FromBinary(Json.FromValue(inputTable)),
jsonbody = anomaly,
bytesbody = Text.ToBinary(jsonbody),
headers = [#”Content-Type” = “application/json”, #”Ocp-Apim-Subscription-Key” = apikey],
bytesresp = Web.Contents(endpoint, [Headers=headers, Content=bytesbody]),
jsonresp = Json.Document(bytesresp),

respTable = Table.FromColumns({

Table.Column(inputTable, “timestamp”)
,Table.Column(inputTable, “value1”)
, Record.Field(jsonresp, “isAnomaly”) as list
, Record.Field(jsonresp, “expectedValues”) as list
, Record.Field(jsonresp, “upperMargins”)as list
, Record.Field(jsonresp, “lowerMargins”) as list
, Record.Field(jsonresp, “isPositiveAnomaly”) as list
, Record.Field(jsonresp, “isNegativeAnomaly”) as list

}, {“timestamp”, “value1”, “isAnomaly”, “expectedValues”, “upperMargin”, “lowerMargin”, “isPositiveAnomaly”, “isNegativeAnomaly”}

respTable1 = Table.AddColumn(respTable , “upperMargins”, (row) => row[expectedValues] + row[upperMargin]),
respTable2 = Table.AddColumn(respTable1 , “lowerMargins”, (row) => row[expectedValues] – row[lowerMargin]),
respTable3 = Table.RemoveColumns(respTable2, “upperMargin”),
respTable4 = Table.RemoveColumns(respTable3, “lowerMargin”),

results = Table.TransformColumnTypes(

{{“timestamp”, type datetime}, {“value1”, type number}, {“isAnomaly”, type logical}, {“isPositiveAnomaly”, type logical}, {“isNegativeAnomaly”, type logical},
{“expectedValues”, type number}, {“upperMargins”, type number}, {“lowerMargins”, type number}}

in results

This code is a bit different from Microsoft documents to support the API.

Now you should see the below result.

the original data in the first two columns, then the boolean column to show is anomaly happen or not, then expected value.

the ISPositiveAnomaly and IsNegativeAnomaly


This is the Preview service so the API can change, What I recommend to make the above code updated is to check the JSON code in the Demo page

I will create a video of this process soon to make it clear

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Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

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