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

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

Introduction Forecasting Techniques
Amazon Forecasting
Amazon SageMaker
simplify what you need to know to deploy the Anaplan on AWS solution
quickly
Agenda

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

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

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

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

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

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.

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

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

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

Amazon Forecast Algorithms
+

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)

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

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

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 - 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.

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

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

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.

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

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

AMAZON FORECAST – Prophet
Prophet
Additive regression model with
Gaussian likelihood
Can find trend, seasonality, cyclical, and holiday effects

• 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

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

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).

Missing data

• 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).

Feature Engineering, Custom Feature

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

Amazon SageMaker Notebooks
• Jupyter notebooks
• Support JupyterLab
• Multiple built-in kernels
• Install external libraries
• Install external kernels
• Integrate with Git
• Sample notebooks

One click in console
Using API/SDK
- OR -
Launch
training
Amazon SageMaker training

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

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/

Thanks!

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

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

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

Similaire à Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learning. (20)

Plus de Amazon Web Services

Plus de Amazon Web Services (20)

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