If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.

1. Supported by the Russian MVP Сommunity
Time Series Anomaly Detection
with .net and Azure
Marco Parenzan
Solution Sales Specialist @ Insight
Microsoft Azure MVP
1nn0va Community Lead

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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

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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
• Conclusions

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Scenario

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

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

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Storage
Account
Azure
IoT Central
Devices
Events
Ingest
The starting point...better...

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Current data path
Data collecting
Data Analysis
Data Report...?
Threshold alarms

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• 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
Threshold anomalies?

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

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

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A bit of theory for Anomaly Detection
in Time Series

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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.

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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.

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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.

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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.

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How to do Time Series Anomaly Detection?
• Statistical Profiling Approach
• This can be done by calculating statistical values like mean or median moving
average of the historical data and using a standard deviation to come up with a
band of statistical values which can define the uppermost bound and the lower
most bound and anything falling beyond these ranges can be an anomaly.
• By Predictive Confidence Level Approach
• One way of doing anomaly detection with time series data is by building a
predictive model using the historical data to estimate and get a sense of the
overall common trend, seasonal or cyclic pattern of the time series data.
• Clustering Based Unsupervised Approach
• Unsupervised approaches are extremely useful for anomaly detection as it does
not require any labelled data, mentioning that a particular data point is an
anomaly.

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• All described is “univariate” anomaly
detection, on a single time serie
• The multivariate anomaly detection
allows detecting anomalies from
groups of metrics
• Dependencies and inter-correlations
between different signals
• News are already announced in this
area, else not yet available
Multivariate anomaly detection
18
#GLOBALAZURE2021

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Data Science for the .NET developer

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• 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.
Data Science and AI for the .NET developer

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ML.NET Components
Anomaly Detection

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Classic Process

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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.

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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

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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]

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• 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
Helping no-data scientits developers (all! )
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet

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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.

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Experimenting with .NET and WPF

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How Data Scientists work

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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

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.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)

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Experimenting ML.NET
with .NET Interactive

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Bring ML.NET to Azure

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.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

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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
●●●

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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

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Bring ML.NET to Azure

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Anomaly Detection As A Service in Azure

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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.

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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.

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Anomaly Detection As A Service

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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.
It seems too much simple…

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Metrics Advisor(preview)

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Conclusions

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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!

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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/
https://azure.microsoft.com/en-us/services/cognitive-services/metrics-advisor/