The document discusses time series anomaly detection using Azure data services, highlighting its application in scenarios like monitoring industrial fridges. It covers the fundamentals of anomaly detection, data science integration for .NET developers through ML.NET, and various Azure services like Cognitive Services for anomaly detection. The conclusions emphasize the importance of starting with existing data, the need for an effective data lake strategy, and the increasing significance of Azure services in the domain.

1. SQL
START!
2021
–
10°
ANNIVERSARY
Deep Dive
Time Series
Anomaly Detection
with different Azure Data Services
Marco Parenzan
@marco_parenzan

2. Partners

3. Marco Parenzan
• Solution Sales Specialist @ Insight
• 1nn0va Community Lead (Pordenone)
• Microsoft Azure MVP
• Profiles
• Linkedin: https://www.linkedin.com/in/marcoparenzan/
• Slideshare: https://www.slideshare.net/marco.parenzan
• GitHub: https://github.com/marcoparenzan

4. Agenda
• Scenario
• Anomaly Detection in Time Series
• Data Science for the .NET developer
• How Data Scientists work
• Bring ML.NET to Azure
• Anomaly Detection As A Service in Azure
• Time Series Anomaly Detection on Big Data (opt 1)
• Time Series Anomaly Detection on Big Data (opt 2)
• Conclusions

5. Scenario

6. Scenario
• In an industrial fridge, you monitor temperatures to check
not the temperature «per se», but to check the healthy of
the plant
From real industrial fridges 

7. With no any specific request...what happens
in production?
Efficiency Anomalies
Batch Streaming

8. Threshold anomalies?
• Threshold Anomalies for a time window
• Slow changing damages
• Fridge is no more efficient
• Threshold alarms are not enough
• Anomalies cannot be just «over a threshold
for some time»...
• Condenser or Evaporator with difficulties
starting
• Distinguish from Opening a door (that is
also an anomaly)
• Or also counting the number of times that
there are peaks (too many times)
• You can considering each of these events
as anomalies that alter the temperature
you measure in different part of the
fridge

9. Storage
Account
IoT Hub
Devices
Events
Ingest
The starting point...

10. How we can evolve...
Function App
Notification
Logic App
Ingest Process
Storage
Account
Azure
IoT Central
Devices
Events

11. A bit of theory
for Anomaly Detection
in Time Series

12. Anomaly Detection
• Anomaly detection is the process of identifying unexpected items or
events in data sets, which differ from the norm.
• And anomaly detection is often applied on unlabeled data which is
known as unsupervised anomaly detection.

13. Time Series
• Definition
• Time series is a sequence of data points recorded in time order, often taken at successive
equally paced points in time.
• Examples
• Stock prices, Sales demand, website traffic, daily temperatures, quarterly sales
• Time series is different from regression analysis because of its time-dependent
nature.
• Auto-correlation: Regression analysis requires that there is little or no autocorrelation in the
data. It occurs when the observations are not independent of each other. For example, in
stock prices, the current price is not independent of the previous price. [The observations
have to be dependent on time]
• Seasonality, a characteristic which we will discuss below.

14. Components of a Time Series
• Trend
• is a general direction in which something is developing or changing. A trend can be
upward(uptrend) or downward(downtrend). It is not always necessary that the increase or
decrease is consistently in the same direction in a given period.
• Seasonality
• Predictable pattern that recurs or repeats over regular intervals. Seasonality is often
observed within a year or less.
• Irregular fluctuation
• These are variations that occur due to sudden causes and are unpredictable. For example the
rise in prices of food due to war, flood, earthquakes, farmers striking etc.

15. Anomaly Detection in Time Series
• In time series data, an anomaly or outlier can be termed as a data
point which is not following the common collective trend or seasonal
or cyclic pattern of the entire data and is significantly distinct from
rest of the data. By significant, most data scientists mean statistical
significance, which in order words, signify that the statistical
properties of the data point is not in alignment with the rest of the
series.
• Anomaly detection has two basic assumptions:
• Anomalies only occur very rarely in the data.
• Their features differ from the normal instances significantly.

16. Data Science for the .NET
developer

17. Data Science and AI for the .NET developer
• ML.NET is first and foremost a framework that you can use
to create your own custom ML models. This custom
approach contrasts with “pre-built AI,” where you use pre-
designed general AI services from the cloud (like many of
the offerings from Azure Cognitive Services). This can work
great for many scenarios, but it might not always fit your
specific business needs due to the nature of the machine
learning problem or to the deployment context (cloud vs.
on-premises).
• ML.NET enables developers to use their existing .NET skills
to easily integrate machine learning into almost any .NET
application. This means that if C# (or F# or VB) is your
programming language of choice, you no longer have to
learn a new programming language, like Python or R, in
order to develop your own ML models and infuse custom
machine learning into your .NET apps.

18. ML.NET Components
Anomaly Detection

19. Classic Process

20. Independent Identically Distributed (iid)
• Data points collected in the time series are independently sampled
from the same distribution (independent identically distributed).
Thus, the value at the current timestamp can be viewed as the value
at the next timestamp in expectation.

21. Singular Spectrum Analysis (SSA)
• This class implements the general anomaly detection transform based
on Singular Spectrum Analysis (SSA). SSA is a powerful framework for
decomposing the time-series into trend, seasonality and noise
components as well as forecasting the future values of the time-
series.
• In principle, SSA performs spectral analysis on the input time-series
where each component in the spectrum corresponds to a trend,
seasonal or noise component in the time-series

22. Spectrum Residual Cnn (SrCnn)
• To monitor the time-series continuously and alert for potential incidents on time
• The algorithm first computes the Fourier Transform of the original data. Then it computes
the spectral residual of the log amplitude of the transformed signal before applying the Inverse
Fourier Transform to map the sequence back from the frequency to the time domain. This
sequence is called the saliency map. The anomaly score is then computed as the relative
difference between the saliency map values and their moving averages. If the score is above a
threshold, the value at a specific timestep is flagged as an outlier.
• There are several parameters for SR algorithm. To obtain a model with good performance, we
suggest to tune windowSize and threshold at first, these are the most important parameters to
SR. Then you could search for an appropriate judgementWindowSize which is no larger than
windowSize. And for the remaining parameters, you could use the default value directly.
• Time-Series Anomaly Detection Service at Microsoft [https://arxiv.org/pdf/1906.03821.pdf]

23. Helping no-data scientits developers (all! )
• Unsupervised Machine
LearningNo labelling
• Automated Training Set for
Anomaly Detection Algorithms
• the algorithms automatically
generates a simulated training
set based non your input data
• Auto(mated) MLfind the best
tuning for you with parameters
and algorithms
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet

24. Some tools required
• .NET 5 + WPF + ML.NET
• Mandatory , the platform where we try to make experiments
• Xplot.Ploty (soon you will understand I use this) https://fslab.org/XPlot/
• XPlot is a cross-platform data visualization package for the F# programming language powered by
popular JavaScript charting libraries Plotly and Google Charts. The library provides a complete
mapping for the configuration options of the underlying libraries and so you get a nice F#
interface that gives you access to the full power of Plotly and Google Charts. The XPlot library can
be used interactively from F# Interactive, but charts can equally easy be embedded in F#
applications and in HTML reports.
• WebView2 https://docs.microsoft.com/en-us/microsoft-edge/webview2/gettingstarted/wpf
• The Microsoft Edge WebView2 control enables you to embed web technologies (HTML, CSS, and
JavaScript) in your native apps. The WebView2 control uses Microsoft Edge (Chromium) as the
rendering engine to display the web content in native apps. With WebView2, you may embed
web code in different parts of your native app. Build all of the native app within a single WebView
instance.

25. Experimenting with .NET and
WPF

26. How Data Scientists work

27. Jupyter
• Evolution and generalization of the seminal role of Mathematica
• In web standards way
• Web (HTTP+Markdown)
• Python adoption (ipynb)
• Written in Java
• Python has an interop bridge...not native (if ever
important)Python is a kernel for Jupyter

28. .NET Interactive and Jupyter
and Visual Studio Code
• .NET Interactive gives C# and F# kernels to Jupyter
• .NET Interactive gives all tools to create your hosting application
independently from Jupyter
• In Visual Studio Code, you have two different notebooks (looking similar
but developed in parallel by different teams)
• .NET Interactive Notebook (by the .NET Interactive Team) that can run also Python
• Jupyter Notebook (by the Azure Data Studio Team – probably) that can run also C#
and F#
• There is a little confusion on that 
• .NET Interactive has a strong C#/F# Kernel...
• ...a less mature infrastructure (compared to Jupiter)

29. Experimenting ML.NET
with .NET Interactive

30. Bring ML.NET to Azure

31. .NET (5) hosting in Azure
Existing apps
.NET web apps (on-premises)
Cloud-Optimized
PaaS
Cloud-Native
PaaS for microservices and serverless
Monolithic / N-Tier
architectures
Monolithic / N-Tier
architectures
Microservices and serverless architectures
Cloud
Infrastructure-Ready
Monolithic / N-Tier
architectures
Relational
Database
VMs
Managed services
On-premises Azure
PaaS for containerized microservices
+ Serverless computing
+ Managed services
And Windows Containers
IaaS
(Infrastructure as a Service)
Azure Azure

32. Functions everywhere
Platform
App delivery
OS
On-premises
Code
App Service on Azure Stack
Windows
●●●
Non-Azure hosts
●●●
●●●
+
Azure Functions
host runtime
Azure Functions
Core Tools
Azure Functions
base Docker image
Azure Functions
.NET Docker image
Azure Functions
Node Docker image
●●●

33. Logic Apps
• Visually design workflows in the
cloud
• Express logic through powerful
control flow
• Connect disparate functions and
APIs
• Utilize declarative definition to
work with CI/CD

34. Bring ML.NET to Azure

35. Anomaly Detection As A Service
in Azure

36. Azure Cognitive Services
• Cognitive Services brings AI within reach of every developer—without requiring
machine-learning expertise. All it takes is an API call to embed the ability to see,
hear, speak, search, understand, and accelerate decision-making into your apps.
Enable developers of all skill levels to easily add AI capabilities to their apps.
• Five areas:
• Decision
• Language
• Speech
• Vision
• Web search
Anomaly Detector
Identify potential problems early on.
Content Moderator
Detect potentially offensive or unwanted
content.
Metrics Advisor PREVIEW
Monitor metrics and diagnose issues.
Personalizer
Create rich, personalized experiences for every
user.

37. Anomaly Detector
• Through an API, Anomaly Detector ingests time-series data of all
types and selects the best-fitting detection model for your data to
ensure high accuracy. Customize the service to detect any level of
anomaly and deploy it where you need it most -- from the cloud to
the intelligent edge with containers. Azure is the only major cloud
provider that offers anomaly detection as an AI service.

38. Anomaly Detection As A Service

39. Time Series Anomaly Detection
on Big Data (opt 1)

40. Azure Synapse Analytics
Limitless analytics service with unmatched time to insight
Platform
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
METASTORE
SECURITY
MANAGEMENT
MONITORING
DATA INTEGRATION
Analytics Runtimes
DEDICATED SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala
Experience Synapse Analytics Studio
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
METASTORE
SECURITY
MANAGEMENT
MONITORING

41. Develop Hub - SQL scripts
SQL Script
Authoring SQL Scripts
Execute SQL script on dedicated SQL pool or serverless
SQL pool
Commit individual SQL script or multiple SQL scripts
through Commit all feature
Language support and intellisense

42. Develop Hub - Notebooks
As notebook cells run, the underlying Spark
application status is shown. Providing
immediate feedback and progress tracking.​

43. Ingest Flat files to tables
Ingest flat file data into Azure Storage (Azure Data Lake Store Gen2)
• When your data sources are on-premises, you need to move the data to
Azure Storage before ingestion.
• Data in other cloud platforms needs to be moved to Azure Storage
before ingestion.
Load from flat files as relational tables within the data warehouse

44. ADLS Gen 2 Filesystem
Ingest - Structuring ADLS Gen2
• Separate storage accounts for each environment: dev, test, & production.
• Use a common folder structure to organize data by degree of refinement.

45. Create External Table As Select (Polybase)
Overview
-- Create a database master key if one does not already exist
CREATE MASTER KEY ENCRYPTION BY PASSWORD = 'S0me!nfo'
;
-- Create a database scoped credential with Azure storage account key as the secret.
CREATE DATABASE SCOPED CREDENTIAL AzureStorageCredential
WITH
IDENTITY = '<my_account>'
, SECRET = '<azure_storage_account_key>'
;
-- Create an external data source with CREDENTIAL option.
CREATE EXTERNAL DATA SOURCE MyAzureStorage
WITH
( LOCATION = 'wasbs://daily@logs.blob.core.windows.net/'
, CREDENTIAL = AzureStorageCredential
, TYPE = HADOOP
)
-- Create an external file format
CREATE EXTERNAL FILE FORMAT MyAzureCSVFormat
WITH (FORMAT_TYPE = DELIMITEDTEXT,
FORMAT_OPTIONS(
FIELD_TERMINATOR = ',',
FIRST_ROW = 2)
--Create an external table
CREATE EXTERNAL TABLE dbo.FactInternetSalesNew
WITH(
LOCATION = '/files/Customer',
DATA_SOURCE = MyAzureStorage,
FILE_FORMAT = MyAzureCSVFormat
)
AS SELECT T1.* FROM dbo.FactInternetSales T1 JOIN dbo.DimCustomer T2
ON ( T1.CustomerKey = T2.CustomerKey )
OPTION ( HASH JOIN );

46. Azure Synapse Analytics

47. Time Series Anomaly Detection
on Big Data (opt 2)
Not here...look for Azure Data Explorer in general with Riccardo Zamana

48. Conclusions

49. Conclusions
• Start simple and bulk: you already have data
• If you have daily data, you need to aggregate (a month?) to have training
• take time for a correct Data Lake strategy
• there is time for realtime
• The right algorithm is the one that gives you what you want to see
• Also professionals make the same (besides REAL data scientists)
• But if you know statistics, if better for you 
• Azure Cognitive Services will become more important
• New Metrics Advisor Service!

50. Thank you!
Marco Parenzan
Solution Sales Specialist @ Insight
Microsoft Azure MVP
1nn0va Community Lead
• https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/
• https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sales-anomaly-detection
• https://github.com/dotnet/interactive
• https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/serve-model-serverless-azure-functions-ml-net
• https://azure.microsoft.com/en-us/services/cognitive-services/metrics-advisor/