SlideShare a Scribd company logo
1 of 46
1© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
How to build Forecasting using ML/DL algorithms
Iona Ekonomi – Senior Solutions Architect
2© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Introduction Forecasting Techniques
Amazon Forecasting
Amazon SageMaker
simplify what you need to know to deploy the Anaplan on AWS solution
quickly
Agenda
3© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
The AWS ML Stack
Broadest and most complete set of Machine Learning capabilities
VISION SPEECH TEXT SEARCH NEW CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS
Amazon SageMaker Ground
Truth
Augmented
AI
SageMaker
Neo
Built-in
algorithms
SageMaker
Notebooks
SageMaker
Experiments
Model
tuning
SageMaker
Debugger
SageMaker
Autopilot
Model
hosting
SageMaker
Model Monitor
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia FPGA
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
AI SERVICES
ML SERVICES
ML FRAMEWORKS & INFRASTRUCTURE
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
SageMaker Studio IDE
NEW
NEW
NEW
4© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
1995 2000 2007 2010 2015 2019
Forecasting at Amazon.com
Using machine learning to solve complex forecasting problems
Use of Machine Learning
High price variability Slow moving productsRegional vs national demand New products Highly seasonal
products
Traditional statistical methods Use of deep learning
15x
Improvement
in accuracy
5© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
6© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Private
forecasting API
Amazon Forecast
The technology that powers the world’s largest ecommerce business
Get started with the console or
API
Point Amazon Forecast to your data
stored in Amazon Simple Storage
Service (Amazon S3)
Automatically train your custom ML
model
Let Amazon Forecast auto select the best one for
your data through AutoML
Generate accurate forecasts
Retrieve forecasts through the console or
private API
Historical data
Related data
Sales, call volume, inventory,
resource demand
Price, promotions, weather
data, custom events
Item metadata
Color, city, country, category,
author, album name
Built-in dataset
(holiday, weekends)
Amazon Forecast
7© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Customized
forecasting API
Inspect
data
Identify
features
Select most
accurate model
from multiple
algorithms
Select
Hyper-
parameters
Host
models
Load
data
Train
models using
multiple
algorithms
Optimize
models
Amazon Forecast
Behind the scenes
Fully managed by Amazon Forecast
Historical data
Related data
sales, call volume, inventory,
resource demand.
Price, promotions, weather
data, custom events
Item metadata
Color, city, country, category,
author, album name
Create dataset
Create predictor (train,
inference, metrics)
Create Forecast
Create Forecast export
Query Forecast
Amazon Forecast
8© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Use of historical data
to predict future
values
Target time-
series
dataset
The primary variable to predict with its
historical values
(demand, sales)
Datasets used for forecasting
Use of related
attributes and
categorical data
Item metadata
(non-time-
varying)
Categorical data that provide more context
about items
(color, city, channel)
Use of known time-
varying data specific
to your business
Related time-
series
dataset
Time-varying related features that may
impact the target value
(price, promotion, weather)
Amazon Forecast
9© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Predictor
• Custom model trained on your data.
• A forecast horizon – how far you want to predicate also called the prediction length.
• the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the
TARGET_TIME_SERIES dataset length.
• Evaluation parameters – How to split a dataset into training and test datasets using
backtest
• Then either you chose the algorithm manually or make it Auto where AWS will try all
algorithms and choice the best one.
• AutoML optimizes the average of the weighted P10, P50 and P90 quantile losses,
and returns the algorithm with the lowest value.
10© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Forecast
• It’s your model deployed on the production on somewhere
on AWS cloud and is fully managed by AWS to match your
demand. And now all you need to call this end point to get
the results using Query Forecast.
• Call the CreateForecast operation to create a forecast.
• During forecast creation, Amazon Forecast trains a model
on the entire dataset before hosting the model and doing
inference.
• This operation creates a forecast for every item (item_id) in
the dataset group that was used to train the predictor.
• After a forecast is created, you can query the forecast or
export it to your Amazon Simple Storage Service (Amazon
S3) bucket.
Private
forecasting API
11© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Visualize the distribution of forecasted values
View probabilistic forecasts
at any quantile in the
console
Retrieve forecasts
through your private API
Export forecasts to .csv
Amazon Forecast
12© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Handles tricky forecasting scenarios
Missing values
Cold start
(new product introduction)
Irregular seasonality
Product discontinuation
Highly spiky data
Sensitivity analysis
(future price change)
Amazon Forecast
13© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Amazon Forecast Algorithms
+
14© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
ARIMA
Auto-regressive integrated
moving average
AMAZON FORECAST - ARIMA
• The last step move from ARMA to ARIMA is
differencing step called integrate
ARIMA(p,d,q).
• So we do it on two stages
• First apply differencing (order d)
• Then ARMA (p,q)
15© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST - ARIMA
Linear regression
• Linear regression attempts to
model the relationship
between two variables by
fitting a linear equation to
observed data.
• WE can use linear regression
in forecast y=mx+b
• The slope of the line is m
(coefficient), and b is the
intercept (the value of y when
x = 0).
Capture the trend and seasonality
16© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST - ARIMA
Autocorrelation
• Some time called serial
correlation.
• Find the correlation between
the series and its past value to
improve the forecast.
• Correlation between pairs of
values at a certain lag.
• Lag-1 autocorrelation : Yt and
Yt-1
• Lag-2 autocorrelation : Yt and
Yt-2
17© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Autoregression (AR Model)
• Capture autocorrelation in a series in an
regression type model and use it to improve
short-term forecasts
key concept is Order.
AR(p)
ARMA(p,q)
Only work for short term Forecast
Autoregression Moving Average (ARMA)
• It require Stationarity -no trend/s
seasonality
• So we can have apply on two stage
1. capture trend using regression.
2. apply AR model to capture
autocorrelation and next forecast error
[Moving Average]
3. Combine the two to get the improve
forecast
AMAZON FORECAST - ARIMA
18© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST - ETS
ETS
(ExponenTial Smoothing)
Error Trend Seasonality
Statistical algorithm that uses exponential
smoothing
Exponential smoothing forecasting : prediction is a weighted sum of past
observations, but the model explicitly uses an exponentially decreasing weight for
past observations.
19© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST - ETS
Smoothing
create an approximating
function that attempts to
capture important patterns in
the data, while leaving out noise
or other fine-scale
structures/rapid phenomena
20© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – ETS
Holt’s exponential smoothing
Smoothing using differencing :
• help in the case of dataset has trends
and seasonality
• Differencing means taking the
difference between two values of the
series
• lag :- means how far apart these two
value are for example lag = 1 mean y(t) -
y(t-1) which help to remove trend.
• lag-M differencing y(t) - y(t-m) useful for
removing seasonality with M seasons.
Holt’s exponential smoothing (double
exponential smoothing ):
• Fore series with trend but no
seasonality.
• 𝐹!"# = 𝐿! + K 𝑇! ( T is the trend )
• 𝑇! = 𝛽 (𝐿! − 𝐿!$% ) + (1- 𝛽) 𝑇!$%
• 0 ≤ 𝛽 ≤ 1 à how fast we update
the trend
• 𝛽à is the trend constant
21© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – ETS
Winter’s exponential smoothing (triple exponential smoothing )
• Fore series with trend &
seasonality
• 𝐹!"# = 𝐿! + K 𝑇! + 𝑆!"#$&
• 𝑆! = γ (𝐿! − 𝐿!$% ) + (1- 𝛾) 𝑆!$%
• 0 ≤ 𝛾 ≤ 1 à how fast we update
the seasonality
• Forecast = most recent
estimated level + trend
+ seasonality.
22© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – NPTS
NPTS
Non-parametric time series
Jan 06
2014
Apr 07
2014
Jul 07
2014
Oct 06
2014
Jan 05
2015
Apr 06
2015
Jul 06
2015
Oct 05
2015
Jan 04
2016
Apr 04
2016
0246810
A Typical Time Series in Large Inventories
23© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – NPTS
Parametric
• Fixed number of parameters
• computationally faster, but makes
stronger assumptions about the data.
• A common example of a parametric
algorithm is linear regression.
• we try to find y=mx+b then we though
the data away and use the equation in
the future to find y
Non-Parametric
• uses a flexible number of parameters
and grows as it learns from more data
• computationally slower
• example is K-nearest neighbour and
kernel regression
• we keep the data and we always come
back and consult the data to find the
right predication
24© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – Prophet
Prophet
Additive regression model with
Gaussian likelihood
Can find trend, seasonality, cyclical, and holiday effects
25© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
• Structural time series model
develop by Facebook became
opensource in Feb 2017.
• Use a very flexible regression
model (somewhat like curve-
fitting)
• Builds model by finding a best
smooth line which as sum of
• Overall growth trend
• Yearly seasonality
• Weekly seasonality
• Holiday effects – X’mas, New Year
etc.
AMAZON FORECAST – Prophet
26© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – DEEPAR+
DeepAR+
Supervised learning algorithm based on
autoregressive RNNs that can produce both
point and probabilistic forecasts .
Based on LSTM Networks
Global model that can use related time series and
attributes
27© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Forecast
Autoregressive history
Covariates
Neural networks are good at leveraging long history to learn its influence on future points, and they can
handle high-dimensionality in the inputs (that is, they can handle many related-items).
AMAZON FORECAST – DEEPAR+
28© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – DEEPAR+
Missing data
29© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
• The DeepAR+ forecasting algorithm has been used internally in Amazon
for mission-critical decisions
•Classical forecasting techniques such as ARIMA and ETS fit one model to
an individual time series. However, in many situations, a set of related
time series have been or can be collected.
•DeepAR+ can train a model over such a set of related time series for
additional insights and increased predictive power
•Requires minimal feature engineering and can produce forecasts that
are either point (amount sold was X) or probabilistic (amount sold was
between X and Y with Z probability).
AMAZON FORECAST – DEEPAR+
30© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AMAZON FORECAST – DEEPAR+
Feature Engineering, Custom Feature
31© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Amazon SageMaker
Fast &
accurate data
labeling
Built-in, high-
performance
algorithms &
notebooks
Build
1
One-click
training
and tuning
Train
Model
optimization
2
Deploy
3
Fully managed
hosting with
auto-scaling and
elastic inference
One-click
deployment
Build, train, and deploy ML models at scale
32© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Amazon SageMaker Notebooks
• Jupyter notebooks
• Support JupyterLab
• Multiple built-in kernels
• Install external libraries
• Install external kernels
• Integrate with Git
• Sample notebooks
33© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
One click in console
Using API/SDK
- OR -
Launch
training
Amazon SageMaker training
34© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Amazon SageMaker
built-in algorithms
Supported
frameworks
AWS Marketplace
algorithms
Model
Data Data Data Data
Orchestration
Built-in
algorithms
AmazonSageMaker
Model Model Model
Orchestration
AmazonSageMaker
Custom script Algorithms or
models
Custom script on
supported frameworks
BYO algorithm and
framework
17 built-in high-
performance
algorithms
Supported frameworks:
Apache MXNet,
TensorFlow, Scikit-learn,
PyTorch, Chainer
Docker containers with your
own algorithms and
frameworks
Third-party algorithms
and models
Supported frameworks
Orchestration
AmazonSageMaker
Custom script
and custom
framework
Orchestration
AmazonSageMaker
Amazon SageMaker training
35© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
36© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
37© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
38© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
39© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
40© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
41© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
42© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
43© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
44© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
45© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
AWS Europe (Milan) Region
On April, 28th AWS expanded its global footprint with the opening of the AWS Infrastructure Region in Italy. The new
Region AWS Europe (Milano) brings advanced cloud technologies that enable opportunities for innovation,
entrepreneurship, and digital transformation. For additional information about services and characteristics of an AWS
Region, you can check the website: aws.amazon.com/local/italy/milan/
46© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Thanks!

More Related Content

What's hot

Leveraging AWS Partner Network (APN) Resources
Leveraging AWS Partner Network (APN) ResourcesLeveraging AWS Partner Network (APN) Resources
Leveraging AWS Partner Network (APN) ResourcesAmazon Web Services
 
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017Amazon Web Services
 
Accelerating Your Cloud Migration Journey with MAP
Accelerating Your Cloud Migration Journey with MAPAccelerating Your Cloud Migration Journey with MAP
Accelerating Your Cloud Migration Journey with MAPAmazon Web Services
 
Cloud Center of Excellence
Cloud Center of ExcellenceCloud Center of Excellence
Cloud Center of ExcellenceJeremy Canale
 
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdf
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdfData & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdf
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdfChris Bingham
 
AWS Cloud Adoption and the Future of Financial Services
AWS Cloud Adoption and the Future of Financial ServicesAWS Cloud Adoption and the Future of Financial Services
AWS Cloud Adoption and the Future of Financial ServicesAmazon Web Services
 
Data Center Migration to the AWS Cloud
Data Center Migration to the AWS CloudData Center Migration to the AWS Cloud
Data Center Migration to the AWS CloudTom Laszewski
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceAmazon Web Services
 
What is Cloud Computing with Amazon Web Services?
What is Cloud Computing with Amazon Web Services?What is Cloud Computing with Amazon Web Services?
What is Cloud Computing with Amazon Web Services?Amazon Web Services
 
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018Amazon Web Services
 
Power up! Mass Migrations at Speed and Scale - Accenture
Power up! Mass Migrations at Speed and Scale - AccenturePower up! Mass Migrations at Speed and Scale - Accenture
Power up! Mass Migrations at Speed and Scale - AccentureAmazon Web Services
 
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...Amazon Web Services
 
Solution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentSolution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentAdobe
 
Azure Migration Program Pitch Deck
Azure Migration Program Pitch DeckAzure Migration Program Pitch Deck
Azure Migration Program Pitch DeckNicholas Vossburg
 
AWS Managed Services and SaaS Partner Programs
AWS Managed Services and SaaS Partner ProgramsAWS Managed Services and SaaS Partner Programs
AWS Managed Services and SaaS Partner ProgramsAmazon Web Services
 
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...An Overview of Best Practices for Large Scale Migrations - AWS Transformation...
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...Amazon Web Services
 

What's hot (20)

Leveraging AWS Partner Network (APN) Resources
Leveraging AWS Partner Network (APN) ResourcesLeveraging AWS Partner Network (APN) Resources
Leveraging AWS Partner Network (APN) Resources
 
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017
Introduction to AWS and Cloud Computing - Module 1 Part 1 - AWSome Day 2017
 
Accelerating Your Cloud Migration Journey with MAP
Accelerating Your Cloud Migration Journey with MAPAccelerating Your Cloud Migration Journey with MAP
Accelerating Your Cloud Migration Journey with MAP
 
Cloud Center of Excellence
Cloud Center of ExcellenceCloud Center of Excellence
Cloud Center of Excellence
 
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdf
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdfData & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdf
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdf
 
Cloud Migration Workshop
Cloud Migration WorkshopCloud Migration Workshop
Cloud Migration Workshop
 
AWS Cloud Adoption and the Future of Financial Services
AWS Cloud Adoption and the Future of Financial ServicesAWS Cloud Adoption and the Future of Financial Services
AWS Cloud Adoption and the Future of Financial Services
 
Data Center Migration to the AWS Cloud
Data Center Migration to the AWS CloudData Center Migration to the AWS Cloud
Data Center Migration to the AWS Cloud
 
Cloud Migration: A How-To Guide
Cloud Migration: A How-To GuideCloud Migration: A How-To Guide
Cloud Migration: A How-To Guide
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container Service
 
Migrating to the Cloud
Migrating to the CloudMigrating to the Cloud
Migrating to the Cloud
 
What is Cloud Computing with Amazon Web Services?
What is Cloud Computing with Amazon Web Services?What is Cloud Computing with Amazon Web Services?
What is Cloud Computing with Amazon Web Services?
 
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018
 
Power up! Mass Migrations at Speed and Scale - Accenture
Power up! Mass Migrations at Speed and Scale - AccenturePower up! Mass Migrations at Speed and Scale - Accenture
Power up! Mass Migrations at Speed and Scale - Accenture
 
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...
Creating an Effective Roadmap for Your Cloud Journey (ENT225-R1) - AWS re:Inv...
 
Solution deck capgemini cloud assessment
Solution deck capgemini cloud assessmentSolution deck capgemini cloud assessment
Solution deck capgemini cloud assessment
 
Computer Vision con AWS
Computer Vision con AWSComputer Vision con AWS
Computer Vision con AWS
 
Azure Migration Program Pitch Deck
Azure Migration Program Pitch DeckAzure Migration Program Pitch Deck
Azure Migration Program Pitch Deck
 
AWS Managed Services and SaaS Partner Programs
AWS Managed Services and SaaS Partner ProgramsAWS Managed Services and SaaS Partner Programs
AWS Managed Services and SaaS Partner Programs
 
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...An Overview of Best Practices for Large Scale Migrations - AWS Transformation...
An Overview of Best Practices for Large Scale Migrations - AWS Transformation...
 

Similar to Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learning.

Data Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksData Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksAmazon Web Services
 
Recomendaciones, predicciones y detección de fraude usando servicios de intel...
Recomendaciones, predicciones y detección de fraude usando servicios de intel...Recomendaciones, predicciones y detección de fraude usando servicios de intel...
Recomendaciones, predicciones y detección de fraude usando servicios de intel...javier ramirez
 
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI services
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI services
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAmazon Web Services
 
Multivariate Time Series
Multivariate Time SeriesMultivariate Time Series
Multivariate Time SeriesApache MXNet
 
AI/ML Powered Personalized Recommendations in Gaming Industry
AI/ML PoweredPersonalized Recommendations in Gaming IndustryAI/ML PoweredPersonalized Recommendations in Gaming Industry
AI/ML Powered Personalized Recommendations in Gaming IndustryHasan Basri AKIRMAK, MSc,ExecMBA
 
Using Tableau and AWS for Fearless Reporting at UMD
Using Tableau and AWS for Fearless Reporting at UMDUsing Tableau and AWS for Fearless Reporting at UMD
Using Tableau and AWS for Fearless Reporting at UMDAmazon Web Services
 
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...Amazon Web Services
 
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
 
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL...
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker -  MCL...Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker -  MCL...
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL...Amazon Web Services
 
Machine Learning, Open Data, and the Future WarFighter
Machine Learning, Open Data, and the Future WarFighterMachine Learning, Open Data, and the Future WarFighter
Machine Learning, Open Data, and the Future WarFighterAmazon Web Services
 
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...Amazon Web Services
 
Air Passenger Prediction Using ARIMA Model
Air Passenger Prediction Using ARIMA Model Air Passenger Prediction Using ARIMA Model
Air Passenger Prediction Using ARIMA Model AkarshAvinash
 
Getting started with streaming analytics: streaming basics (1 of 3)
Getting started with streaming analytics: streaming basics (1 of 3)Getting started with streaming analytics: streaming basics (1 of 3)
Getting started with streaming analytics: streaming basics (1 of 3)javier ramirez
 
Getting started with streaming analytics
Getting started with streaming analyticsGetting started with streaming analytics
Getting started with streaming analyticsjavier ramirez
 
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
 
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
 
Deep Dive Amazon SageMaker
Deep Dive Amazon SageMakerDeep Dive Amazon SageMaker
Deep Dive Amazon SageMakerCobus Bernard
 
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...Amazon Web Services
 
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...Amazon Web Services
 
Building a Modern Data Warehouse - Deep Dive on Amazon Redshift
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftBuilding a Modern Data Warehouse - Deep Dive on Amazon Redshift
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftAmazon Web Services
 

Similar to Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learning. (20)

Data Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech TalksData Transformation Patterns in AWS - AWS Online Tech Talks
Data Transformation Patterns in AWS - AWS Online Tech Talks
 
Recomendaciones, predicciones y detección de fraude usando servicios de intel...
Recomendaciones, predicciones y detección de fraude usando servicios de intel...Recomendaciones, predicciones y detección de fraude usando servicios de intel...
Recomendaciones, predicciones y detección de fraude usando servicios de intel...
 
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI services
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI services
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI services
 
Multivariate Time Series
Multivariate Time SeriesMultivariate Time Series
Multivariate Time Series
 
AI/ML Powered Personalized Recommendations in Gaming Industry
AI/ML PoweredPersonalized Recommendations in Gaming IndustryAI/ML PoweredPersonalized Recommendations in Gaming Industry
AI/ML Powered Personalized Recommendations in Gaming Industry
 
Using Tableau and AWS for Fearless Reporting at UMD
Using Tableau and AWS for Fearless Reporting at UMDUsing Tableau and AWS for Fearless Reporting at UMD
Using Tableau and AWS for Fearless Reporting at UMD
 
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...
Best practices for Running Spark jobs on Amazon EMR with Spot Instances | AWS...
 
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Machine learning for developers & data scientists with Amazon SageMaker - AIM...
Machine learning for developers & data scientists with Amazon SageMaker - AIM...
 
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL...
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker -  MCL...Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker -  MCL...
Construindo Aplicações Deep Learning com TensorFlow e Amazon SageMaker - MCL...
 
Machine Learning, Open Data, and the Future WarFighter
Machine Learning, Open Data, and the Future WarFighterMachine Learning, Open Data, and the Future WarFighter
Machine Learning, Open Data, and the Future WarFighter
 
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...
How Intuit TurboTax Ran Entirely on AWS for 2017 Taxes (ARC307) - AWS re:Inve...
 
Air Passenger Prediction Using ARIMA Model
Air Passenger Prediction Using ARIMA Model Air Passenger Prediction Using ARIMA Model
Air Passenger Prediction Using ARIMA Model
 
Getting started with streaming analytics: streaming basics (1 of 3)
Getting started with streaming analytics: streaming basics (1 of 3)Getting started with streaming analytics: streaming basics (1 of 3)
Getting started with streaming analytics: streaming basics (1 of 3)
 
Getting started with streaming analytics
Getting started with streaming analyticsGetting started with streaming analytics
Getting started with streaming analytics
 
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...
 
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...
 
Deep Dive Amazon SageMaker
Deep Dive Amazon SageMakerDeep Dive Amazon SageMaker
Deep Dive Amazon SageMaker
 
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...
[NEW LAUNCH!] Introducing Amazon Elastic Inference: Reduce Deep Learning Infe...
 
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...
Analyze Amazon CloudFront and Lambda@Edge Logs to Improve Customer Experience...
 
Building a Modern Data Warehouse - Deep Dive on Amazon Redshift
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftBuilding a Modern Data Warehouse - Deep Dive on Amazon Redshift
Building a Modern Data Warehouse - Deep Dive on Amazon Redshift
 

More from Amazon Web Services

Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSAmazon Web Services
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareAmazon Web Services
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAmazon Web Services
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWSAmazon Web Services
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckAmazon Web Services
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without serversAmazon Web Services
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
 
Come costruire un'architettura Serverless nel Cloud AWS
Come costruire un'architettura Serverless nel Cloud AWSCome costruire un'architettura Serverless nel Cloud AWS
Come costruire un'architettura Serverless nel Cloud AWSAmazon Web Services
 
AWS Serverless per startup: come innovare senza preoccuparsi dei server
AWS Serverless per startup: come innovare senza preoccuparsi dei serverAWS Serverless per startup: come innovare senza preoccuparsi dei server
AWS Serverless per startup: come innovare senza preoccuparsi dei serverAmazon Web Services
 
Crea dashboard interattive con Amazon QuickSight
Crea dashboard interattive con Amazon QuickSightCrea dashboard interattive con Amazon QuickSight
Crea dashboard interattive con Amazon QuickSightAmazon Web Services
 
Costruisci modelli di Machine Learning con Amazon SageMaker Autopilot
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotCostruisci modelli di Machine Learning con Amazon SageMaker Autopilot
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
 
Migra le tue file shares in cloud con FSx for Windows
Migra le tue file shares in cloud con FSx for Windows Migra le tue file shares in cloud con FSx for Windows
Migra le tue file shares in cloud con FSx for Windows Amazon Web Services
 

More from Amazon Web Services (20)

Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWS
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot
 
Open banking as a service
Open banking as a serviceOpen banking as a service
Open banking as a service
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatare
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e web
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWS
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch Deck
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without servers
 
Fundraising Essentials
Fundraising EssentialsFundraising Essentials
Fundraising Essentials
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
 
Come costruire un'architettura Serverless nel Cloud AWS
Come costruire un'architettura Serverless nel Cloud AWSCome costruire un'architettura Serverless nel Cloud AWS
Come costruire un'architettura Serverless nel Cloud AWS
 
AWS Serverless per startup: come innovare senza preoccuparsi dei server
AWS Serverless per startup: come innovare senza preoccuparsi dei serverAWS Serverless per startup: come innovare senza preoccuparsi dei server
AWS Serverless per startup: come innovare senza preoccuparsi dei server
 
Crea dashboard interattive con Amazon QuickSight
Crea dashboard interattive con Amazon QuickSightCrea dashboard interattive con Amazon QuickSight
Crea dashboard interattive con Amazon QuickSight
 
Costruisci modelli di Machine Learning con Amazon SageMaker Autopilot
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotCostruisci modelli di Machine Learning con Amazon SageMaker Autopilot
Costruisci modelli di Machine Learning con Amazon SageMaker Autopilot
 
Migra le tue file shares in cloud con FSx for Windows
Migra le tue file shares in cloud con FSx for Windows Migra le tue file shares in cloud con FSx for Windows
Migra le tue file shares in cloud con FSx for Windows
 

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learning.

  • 1. 1© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | How to build Forecasting using ML/DL algorithms Iona Ekonomi – Senior Solutions Architect
  • 2. 2© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Introduction Forecasting Techniques Amazon Forecasting Amazon SageMaker simplify what you need to know to deploy the Anaplan on AWS solution quickly Agenda
  • 3. 3© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH NEW CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Amazon SageMaker Ground Truth Augmented AI SageMaker Neo Built-in algorithms SageMaker Notebooks SageMaker Experiments Model tuning SageMaker Debugger SageMaker Autopilot Model hosting SageMaker Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE NEW NEW NEW
  • 4. 4© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | 1995 2000 2007 2010 2015 2019 Forecasting at Amazon.com Using machine learning to solve complex forecasting problems Use of Machine Learning High price variability Slow moving productsRegional vs national demand New products Highly seasonal products Traditional statistical methods Use of deep learning 15x Improvement in accuracy
  • 5. 5© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 6. 6© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Private forecasting API Amazon Forecast The technology that powers the world’s largest ecommerce business Get started with the console or API Point Amazon Forecast to your data stored in Amazon Simple Storage Service (Amazon S3) Automatically train your custom ML model Let Amazon Forecast auto select the best one for your data through AutoML Generate accurate forecasts Retrieve forecasts through the console or private API Historical data Related data Sales, call volume, inventory, resource demand Price, promotions, weather data, custom events Item metadata Color, city, country, category, author, album name Built-in dataset (holiday, weekends) Amazon Forecast
  • 7. 7© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Customized forecasting API Inspect data Identify features Select most accurate model from multiple algorithms Select Hyper- parameters Host models Load data Train models using multiple algorithms Optimize models Amazon Forecast Behind the scenes Fully managed by Amazon Forecast Historical data Related data sales, call volume, inventory, resource demand. Price, promotions, weather data, custom events Item metadata Color, city, country, category, author, album name Create dataset Create predictor (train, inference, metrics) Create Forecast Create Forecast export Query Forecast Amazon Forecast
  • 8. 8© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Use of historical data to predict future values Target time- series dataset The primary variable to predict with its historical values (demand, sales) Datasets used for forecasting Use of related attributes and categorical data Item metadata (non-time- varying) Categorical data that provide more context about items (color, city, channel) Use of known time- varying data specific to your business Related time- series dataset Time-varying related features that may impact the target value (price, promotion, weather) Amazon Forecast
  • 9. 9© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Predictor • Custom model trained on your data. • A forecast horizon – how far you want to predicate also called the prediction length. • the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length. • Evaluation parameters – How to split a dataset into training and test datasets using backtest • Then either you chose the algorithm manually or make it Auto where AWS will try all algorithms and choice the best one. • AutoML optimizes the average of the weighted P10, P50 and P90 quantile losses, and returns the algorithm with the lowest value.
  • 10. 10© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Forecast • It’s your model deployed on the production on somewhere on AWS cloud and is fully managed by AWS to match your demand. And now all you need to call this end point to get the results using Query Forecast. • Call the CreateForecast operation to create a forecast. • During forecast creation, Amazon Forecast trains a model on the entire dataset before hosting the model and doing inference. • This operation creates a forecast for every item (item_id) in the dataset group that was used to train the predictor. • After a forecast is created, you can query the forecast or export it to your Amazon Simple Storage Service (Amazon S3) bucket. Private forecasting API
  • 11. 11© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Visualize the distribution of forecasted values View probabilistic forecasts at any quantile in the console Retrieve forecasts through your private API Export forecasts to .csv Amazon Forecast
  • 12. 12© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Handles tricky forecasting scenarios Missing values Cold start (new product introduction) Irregular seasonality Product discontinuation Highly spiky data Sensitivity analysis (future price change) Amazon Forecast
  • 13. 13© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon Forecast Algorithms +
  • 14. 14© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | ARIMA Auto-regressive integrated moving average AMAZON FORECAST - ARIMA • The last step move from ARMA to ARIMA is differencing step called integrate ARIMA(p,d,q). • So we do it on two stages • First apply differencing (order d) • Then ARMA (p,q)
  • 15. 15© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST - ARIMA Linear regression • Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. • WE can use linear regression in forecast y=mx+b • The slope of the line is m (coefficient), and b is the intercept (the value of y when x = 0). Capture the trend and seasonality
  • 16. 16© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST - ARIMA Autocorrelation • Some time called serial correlation. • Find the correlation between the series and its past value to improve the forecast. • Correlation between pairs of values at a certain lag. • Lag-1 autocorrelation : Yt and Yt-1 • Lag-2 autocorrelation : Yt and Yt-2
  • 17. 17© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Autoregression (AR Model) • Capture autocorrelation in a series in an regression type model and use it to improve short-term forecasts key concept is Order. AR(p) ARMA(p,q) Only work for short term Forecast Autoregression Moving Average (ARMA) • It require Stationarity -no trend/s seasonality • So we can have apply on two stage 1. capture trend using regression. 2. apply AR model to capture autocorrelation and next forecast error [Moving Average] 3. Combine the two to get the improve forecast AMAZON FORECAST - ARIMA
  • 18. 18© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST - ETS ETS (ExponenTial Smoothing) Error Trend Seasonality Statistical algorithm that uses exponential smoothing Exponential smoothing forecasting : prediction is a weighted sum of past observations, but the model explicitly uses an exponentially decreasing weight for past observations.
  • 19. 19© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST - ETS Smoothing create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena
  • 20. 20© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – ETS Holt’s exponential smoothing Smoothing using differencing : • help in the case of dataset has trends and seasonality • Differencing means taking the difference between two values of the series • lag :- means how far apart these two value are for example lag = 1 mean y(t) - y(t-1) which help to remove trend. • lag-M differencing y(t) - y(t-m) useful for removing seasonality with M seasons. Holt’s exponential smoothing (double exponential smoothing ): • Fore series with trend but no seasonality. • 𝐹!"# = 𝐿! + K 𝑇! ( T is the trend ) • 𝑇! = 𝛽 (𝐿! − 𝐿!$% ) + (1- 𝛽) 𝑇!$% • 0 ≤ 𝛽 ≤ 1 à how fast we update the trend • 𝛽à is the trend constant
  • 21. 21© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – ETS Winter’s exponential smoothing (triple exponential smoothing ) • Fore series with trend & seasonality • 𝐹!"# = 𝐿! + K 𝑇! + 𝑆!"#$& • 𝑆! = γ (𝐿! − 𝐿!$% ) + (1- 𝛾) 𝑆!$% • 0 ≤ 𝛾 ≤ 1 à how fast we update the seasonality • Forecast = most recent estimated level + trend + seasonality.
  • 22. 22© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – NPTS NPTS Non-parametric time series Jan 06 2014 Apr 07 2014 Jul 07 2014 Oct 06 2014 Jan 05 2015 Apr 06 2015 Jul 06 2015 Oct 05 2015 Jan 04 2016 Apr 04 2016 0246810 A Typical Time Series in Large Inventories
  • 23. 23© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – NPTS Parametric • Fixed number of parameters • computationally faster, but makes stronger assumptions about the data. • A common example of a parametric algorithm is linear regression. • we try to find y=mx+b then we though the data away and use the equation in the future to find y Non-Parametric • uses a flexible number of parameters and grows as it learns from more data • computationally slower • example is K-nearest neighbour and kernel regression • we keep the data and we always come back and consult the data to find the right predication
  • 24. 24© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – Prophet Prophet Additive regression model with Gaussian likelihood Can find trend, seasonality, cyclical, and holiday effects
  • 25. 25© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | • Structural time series model develop by Facebook became opensource in Feb 2017. • Use a very flexible regression model (somewhat like curve- fitting) • Builds model by finding a best smooth line which as sum of • Overall growth trend • Yearly seasonality • Weekly seasonality • Holiday effects – X’mas, New Year etc. AMAZON FORECAST – Prophet
  • 26. 26© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – DEEPAR+ DeepAR+ Supervised learning algorithm based on autoregressive RNNs that can produce both point and probabilistic forecasts . Based on LSTM Networks Global model that can use related time series and attributes
  • 27. 27© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Forecast Autoregressive history Covariates Neural networks are good at leveraging long history to learn its influence on future points, and they can handle high-dimensionality in the inputs (that is, they can handle many related-items). AMAZON FORECAST – DEEPAR+
  • 28. 28© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – DEEPAR+ Missing data
  • 29. 29© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | • The DeepAR+ forecasting algorithm has been used internally in Amazon for mission-critical decisions •Classical forecasting techniques such as ARIMA and ETS fit one model to an individual time series. However, in many situations, a set of related time series have been or can be collected. •DeepAR+ can train a model over such a set of related time series for additional insights and increased predictive power •Requires minimal feature engineering and can produce forecasts that are either point (amount sold was X) or probabilistic (amount sold was between X and Y with Z probability). AMAZON FORECAST – DEEPAR+
  • 30. 30© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AMAZON FORECAST – DEEPAR+ Feature Engineering, Custom Feature
  • 31. 31© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Fast & accurate data labeling Built-in, high- performance algorithms & notebooks Build 1 One-click training and tuning Train Model optimization 2 Deploy 3 Fully managed hosting with auto-scaling and elastic inference One-click deployment Build, train, and deploy ML models at scale
  • 32. 32© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker Notebooks • Jupyter notebooks • Support JupyterLab • Multiple built-in kernels • Install external libraries • Install external kernels • Integrate with Git • Sample notebooks
  • 33. 33© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | One click in console Using API/SDK - OR - Launch training Amazon SageMaker training
  • 34. 34© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Amazon SageMaker built-in algorithms Supported frameworks AWS Marketplace algorithms Model Data Data Data Data Orchestration Built-in algorithms AmazonSageMaker Model Model Model Orchestration AmazonSageMaker Custom script Algorithms or models Custom script on supported frameworks BYO algorithm and framework 17 built-in high- performance algorithms Supported frameworks: Apache MXNet, TensorFlow, Scikit-learn, PyTorch, Chainer Docker containers with your own algorithms and frameworks Third-party algorithms and models Supported frameworks Orchestration AmazonSageMaker Custom script and custom framework Orchestration AmazonSageMaker Amazon SageMaker training
  • 35. 35© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 36. 36© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 37. 37© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 38. 38© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 39. 39© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 40. 40© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 41. 41© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 42. 42© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 43. 43© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 44. 44© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved |
  • 45. 45© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | AWS Europe (Milan) Region On April, 28th AWS expanded its global footprint with the opening of the AWS Infrastructure Region in Italy. The new Region AWS Europe (Milano) brings advanced cloud technologies that enable opportunities for innovation, entrepreneurship, and digital transformation. For additional information about services and characteristics of an AWS Region, you can check the website: aws.amazon.com/local/italy/milan/
  • 46. 46© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | Thanks!