Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. Come learn more.

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IntroducingAmazon Forecast
(Preview)
Bindu Reddy
GM, AWS AI
A I M 3 4 4

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Agenda
Overview of service
Science behind Amazon Forecast
Customer presentation ‒ 21st Century Fox

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Time-series forecasting is the science of predicting future points in a time-
series, given historical data
Applicable across multiple domains

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Accuracy is the most important factor in forecasting
A recent study of 15 US companies revealed that 15% improvement in accuracy
leads to 3% improvement in pre-tax profit*
Under forecasting leads to
lost opportunity
Over-forecasting leads
to wasted resources
*http://demand-planning.com/2018/07/12/how-much-does-forecasting-software-cost/*

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Traditional methods struggle with real-world forecasting complications
Can’t handle
seasonality

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Using deep learning increasesforecastaccuracy

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Introducing Amazon Forecast: An automated forecasting platform
Draws from 20 years of experience in forecasting at Amazon
Improves accuracy by up to 50% vs. traditional models
Comes with multiple deep learning-based algorithms along with classical
methods like ARIMA, ETS, and Prophet

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Amazon Forecast solves hard problems in
forecasting
Predicts spikes
accurately!
Generates forecasts
for new items
Learns relationships
between multiple
related time-series
Incorporates external
data (holidays,
promotions, and
so on)

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Automated time-series forecasting platform
Custom models –
no data sharing
Forecast
any time-series
Visualize and
override forecasts
Easily export to
Oracle, SAP

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Applicable across multipledifferent domains

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Set up your
data pipelines
Generate
forecasts
Deploy
model
Generate a custom deep learning model with five clicks
Compare accuracy
metrics across models
Choose AutoML or one
of the Amazon
Forecast pre-built
algorithms

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Diving alittledeeper …

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Amazon Forecastdataingestion
There are three types of datasets in Amazon Forecast:
Item metadataTarget time-series Related time-series
Related time-
series such as
price, web-hits,
etc.
Historic time-
series data of
items to
forecast
Attributes of the
item such as
category, genre,
and brand

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Traincustommodels once you ingestdata
Use AutoML or pick a
predefined algorithm

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Key metricsreported byAmazon Forecast
Amazon Forecast creates probabilistic forecasts (that is, forecasts for
specific prediction intervals)

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Deploy predictors thatmeetaccuracyrequirements
Deploy your trained
predictor
• Deploy a predictor and generate
Forecasts
• Use an API or schedule forecasts to be
generated on a regular basis

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Intuitiveand easy-to-useconsole

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Visualizeforecastsacross predictors

19. ”We have been using Amazon Forecast to predict demand for over
50,000 different products, using Amazon Forecast’s state-of-the-art
deep learning algorithms that we can use right out of the box.
Amazon Forecast takes care of all the heavy lifting of setting up
pipelines, re-training schedules, and re-generating forecasts, so we
can experiment with hundreds of models very easily.”
Fernando Crocer,
Head of Analytics, Mercado Libre

20. ”AmazonForecasthasbeenappliedtoCJLogistics’parcelvolumeforecastingprocesstooptimize
theamountofhumanresources,transportation,andwarehousespaceweprovisiontomeet
provisiontomeetdemand.AmazonForecastallowsustousesophisticatedmachinelearning-
machinelearning-basedforecastingtechniqueswithoutbuildingourownsystem.Looking
system.Lookingforward,wehaveaclearvisionofincreasingouroperationalefficiencybyusing
efficiencybyusingAmazonForecast.”
YoungSoo Kim,
Vice President of TES Strategy Unit, CJ Logistics

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Agenda
Overview of service
Pre-built algorithms and science behind Amazon Forecast
Customer presentations
Amazon Forecast at 21st Century Fox

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23. “It is difficult to make predictions,
especially about the future.”
Danish Proverb

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Forecasting inanutshell

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Forecasting inanutshell

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Forecasting inanutshell

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Amazon Forecastalgorithmskeyfeatures
1. Probabilistic forecasts
2. Learning across time series
3. Learning with covariates

28. “All forecasts are wrong,
but some are useful.”
Paraphrasing George E. P. Box

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Probabilisticforecasts

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Probabilisticforecasts

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Probabilisticforecasts

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Probabilisticforecasts

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Probabilisticforecasts
• Quantification of uncertainty
• Support optimal decision making
• Make “wrong” forecasts useful
• All Amazon Forecast algorithms
support generating probabilistic
forecasts
• Forecasts can be obtained for
different quantiles of the predictive
distribution

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Traditionaltime-seriesmodels

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Traditionaltime-seriesmodels

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Traditionaltime-seriesmodels

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Traditionaltime-seriesmodels
• Independent forecasts
• Strong structural assumptions
• De-facto industry standard
• Well-understood, > 50 yrs. research
• High data efficiency
• Data must match the structural
assumptions
• Cannot identify patterns across time
series

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Discovering sharedpatterns withdeep learning

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Deep learning time-seriesmodels
• Global models: identify patterns
using all available time series
• Group-dependent seasonality and lifecycle
• Behavior in response to covariate inputs
• Weak structural assumptions
• Can be significantly more accurate
than traditional methods
• Can easily incorporate and learn
from rich metadata
• Support cold-start forecasts for new
items

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Learning withcovariates

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Learning withcovariates
• Additional inputs can
• Explain historical data
• Drive forecast behavior
• Examples from retail demand
forecasting
• Price information
• Information about promotions
• Out-of-stock information
• Web page views
• Categorical inputs can be used to
identify group-level patterns
Fashion
Women’s
Clothing
Shoes
Watches
Men’s
Clothing
Shoes
Watches
Girls'
Clothing
Shoes
Watches
Boys'
Clothing
Shoes
Watches

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Amazon Forecast Customer Story

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Introduction toFox DataAnalytics
+ +
+
Content
Commercial
AI & ML
Consumer

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Usecasefor time-seriesforecasting
• TV broadcast audience
forecasting
• Cable channels audience
forecasting
• Digital and VOD audience
forecasting

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Whyis audience forecasting important for us?
Better forecasting will improve:
• Financial planning
• Campaign planning and creation
• Campaign pacing and delivery
• Reduce liability
• Schedule allocation

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Whatmakes thisproblem speciallycomplex?
Content Metadata
Audience cannibalization
from Competition
Type of program, sports teams, social media buzz, and so on
Specially in Broadcast and Cable TV

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Existingapproaches to theproblem
Traditional
ARIMA

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Existingapproaches to theproblem
Traditional
ARIMA
• Cannot include content
metadata
• Does not consider
relationships with
audience competition

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Existingapproaches to theproblem
Traditional
ARIMA
Regression
XGBoost
• Cannot include content
metadata
• Does not consider
relationships with
Audience competition

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Existingapproaches to theproblem
Traditional
ARIMA
Regression
XGBoost
• Cannot include content
metadata.
• Does not consider
relationships with
Audience competition
• Does not consider the
sequential nature of the
data

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Existingapproaches to theproblem
Traditional
ARIMA
Deep Learning
RNN
Regression
XGBoost
• Cannot include content
metadata.
• Does not consider
relationships with
Audience competition
• Does not consider the
sequential nature of the
data

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Existingapproaches to theproblem
Traditional
ARIMA
Deep Learning
RNN
Regression
XGBoost
• Difficult to optimize
hyperparameters
• Cannot include content
metadata.
• Does not consider
relationships with
Audience competition
• Does not consider the
sequential nature of the
data

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Expected benefits fromAmazon Forecast
Model CreationData Preparation Model Evaluation
• Automated implementation
of best practices for data
preparation
• Out of the box support for state-of-
the-art Deep Learning models for
time series forecasting
• Automated Hyperparameter
Tuning
• Quickly and easily compare
multiple modeling
approaches
• Automated selection of best
model
1
2
3
Improved Model Performance

54. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Bindu Reddy
bindu@amazon.com

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