Prof. Dr. Michael Feindt Karlsruhe Institute of Technology Founder & Chief Scientific Advisor, Blue Yonder GmbH&Co KG

NeuroBayes® – Big Data Predictive Analytics for High Energy Physics & "Real Life“

WHAT WILL HAPPEN?

Blue Yonder provides predictions --- based on data (scientifically sound – with quantified uncertainty -- testable and falsifiable,

WHAT HAS HAPPENED? WHY?

DATA

PATTERN PATTERN

PREDICTIONS

Data Mining and conventional Business Intelligence Predictive Analytics

Influence on business performance

Blue Yonder is unique

Predictive Analytics Suite with a combination of statistical algorithms and neural networks

Experienced, award-winning physicists and computer scientists from renowned institutions such as CERN make up the development team : >70 Ph.D.s

100 120 Sales

E(X)

Probability densityP

Optimal order quantity

General Overview

NeuroBayes®

Sales Forecasts

Fraud Detection

Insurance Premium

Optimization

Patient‘s treatment

optimization

Sports event predictions

....

Fundamental Research

Our roots: 30 years of elementary particle physics, peaking at the LHC at CERN. Built to understand how exactly our universe works.

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LHC: 27km circumference

CERN (1960)

CERN (2005)

Photo: CERN

Our Background: High Energy Physics Fundamental research at the forefront of science

The Large Hadron Collider – 100m under ground, 27km circumference

Photo: CERN

Very strong worldwide competition on getting results from data very strong statistical methods: fast & robust multivariate algos

NeuroBayes® Fundamental research at the forefront of science

Invented in 2000 for reconstruction of b quark fragmentation in DELPHI experiment. Further development in Phi-T, later Blue Yonder. Several hundred successful applications in DELPHI, CDF II, Belle, CMS, ATLAS, LHCb, H1, AMS experiments. More than 400 men-years development Robust and fast algorithm for reconstruction (= prediction) of •  conditional probability densities •  classifications with extreme generalization ability by means of Bayesian regularization. Output

Input

Sign

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con

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Postprocessing

Preprocessing

NeuroBayes example: The LHCb trigger very fast intelligent decisions with NeuroBayes

At the LHC (CERN) – per experiment: 40 000 000 events per second, which translates into 1 PetaByte (1,000,000,000,000,000 Byte) per second raw data

At the LHCb experiment 30 000 instances of NeuroBayes running real-time 24/7 filter out the „interesting“ events without introducing lifetime bias

Photo: CERN

But only 1 PB of interesting data per year can be stored. Need online reduction by 1 : 10,000,000

NeuroBayes example: Full reconstruction of B mesons at the Japanese B factory experiment Belle Fundamental research at the forefront of science

Ø  Belle experiment at KEK/Japan Ø  400 physicists from whole world Ø  10 years of data taking and analysis Ø  World record luminosity Ø  > 100 publications

Ø  Automatic hierarchical reconstruction system built from 72 NeuroBayes networks reconstructed about 1100 different reactions with a factor 2 larger efficiency than all analyses before

Ø  Much cleaner signal

Ø  Work performed by 2 PhD and 1 master student

Ø  Corresponds to 500 “normal” PhD theses

Ø  Corresponds to another 10 years of data taking

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Goal: find (classify) all relevant pixel information

Big Data challenge: process approx. 10G bit per s

Solution: NeuroBayes on Hardware

Future: intelligent decisions directly on sensor (Belle II pixel detector), before big data reaches any computer …

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NeuroBayes @ hardware*:

200 million decisions per second

à 5ns for one decision

BELLE 2 Experiment :

utilizes 40 boards:

à8 billion decisions per second

*features dedicated hardware board:

»  NeuroBayes on FPGA

»  Field Programmable Gate Array: (XILINX Virtex6 VLX75T)

»  Clock frequency: 250 MHz

»  Approx. 1 decision per clock cycle (fully pipelined architecture)

»  Probability decision output possible

Use all available and relevant information as input, e.g. measurements from the various sub-detectors, …

NeuroBayes will extract statistically significant patterns in the data to derive the prediction.

Prediction will return the best estimator for a measurement including a statistically sound estimation of the expected spread.

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Particle Property

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NeuroBayes from Science to Industry Predictive Analytics in High Energy Physics

Use all available and relevant information as input, e.g. article properties, previous sales, etc

NeuroBayes will extract statistically significant patterns in the data to derive the prediction.

Prediction will return e.g. the most probable sales rate including a statistically sound estimation of the expected spread.

Article size

Picture size

colour

Previous sales

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red

brand

price 19,9

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Prediction sales

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NeuroBayes from Science to Industry Predictive Analytics in industry

e.g. Retail

NeuroBayes allows data-driven analysis and forecasts – both in science and industry

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BIG

DATA

MCKINSEY

Growth of the data volume

Each day, companies receive giant quantities of structured and unstructured data from different sources

PREDICTIVE ANALYTICS

MCKINSEY

Increase of operating margins

We are familiar with your questions

Sample recognition of data for the prediction of risks and development to provide forward-looking bases for decision-making

+ 40% + 60%

We are the pioneers

SOCIAL MEDIA

PRODUCT RECOMMENDATION

RETARGETING

MERCHANDISE PLANNING

CUSTOMER ANALYSIS

SALES PREDICTIONS

PREDICTION OF RISKS

DYNAMIC PRICING € €

PREDICTIVE MAINTENANCE

AREAS OF APPLICATION

Technology Overview

NEUROBAYES SYSTEM

High Performance Data Transformation

Trainer

Industry- specific Output

Pred

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Simulator (industry-specific)

OPERATIONAL SYSTEM (Environment)

Expert

OUTPUT

EXPERTISE (Industry-specific models)

HISTORICAL TRANSACTION DATA

REAL-TIME FEED R >

R > BATCH INPUT

WEB UI & SERVICES

STREAM-BASED INPUT

NeuroBayes - a neural network of the 2nd generation and much more

NeuroBayes is an algorithm capable of forecasting whole probability density functions

average patient

individual patient

NeuroBayes System Working principle

Probability density function for target quantity t

Historic Data

Record a = ... b = ... c = ... .... t = …!

NeuroBayes Teacher

NeuroBayes Expert

Current Data

Record a = ... b = ... c = ... .... t = ?

Expertise

Expert System

Forecasts conditional on many features

► The forecast depends on several features which themselves have several manifestations

► Features may be arbitrarily correlated

► Features can be ordered / unordered sets and continuous variables

This results in a complex & high-dimensional space – which is impossible to treat with classical methods

Example: What‘s the right dose for a patient, if she is 56 years old, slightly overweight, works out on 2 days a week, enjoys late dinners, has been treated for 2 other diseases already, etc, etc,

Preprocessing

The preprocessing is an extremely important process before training a network It involves steps like:

Ø  smoothing out statistical fluctuations and outliers in input variables Ø  transforming variables to unified characteristics (mean, width) Ø  Decorrelate variables Ø  Find variables with significant impact / throw out others

The benefits of a powerful preprocessing algorithm involve

Ø  increased robustness Ø  increased network training results (minima easier to find) Ø  increased training speed

Training a network

The following process describes how a Neural Net is trained: 1.  Start with initial values 2.  Measure correct forecasts 3.  Vary the weights in order to increase correct forecasts 4.  After some loops, an optimal set of weights will (hopefully) be found 5.  Save the weights and the topology => This is your expertise (!)

► This method corresponds to an optimization in a high-dimensional and complex space – an extremely hard task (!)

► NB: Finding the global maximum can never be guaranteed ► NB: This is why the preprocessing is so important, it creates a smooth surface

Illustration of the optimization problem in two dimensions

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Find the deepest valley •  in only two dimensons •  you cannot look at each valley •  valleys are not smooth •  you only have limited time

Now imagine this task for 100 dimensions

•  only local minima can be found

What is so special about forecasting whole density functions?

► Shape of the distribution becomes visible (e.g. non-gaussian) ► Uncertainty of the forecast becomes visible and can be extracted

as a number (variance, MAD of dist.) ► Different estimators may be used, such as: mean, modus,

median, p% quantile

Neural Networks – concepts adopted from neuro science

Ø  The human brain solves complex problems very efficiently, detects patterns and stores information (memory)

Ø  It consists of approximately 1011 neurons and1014 connections

Ø  Oversimplied:

Neurons start sending signals when other

neurons reach a certain activity threshold

NeuroBayes output interpretable as a Bayesian posterior

Evidence Posterior

Prior Likelihood

NB1: Posterior ist the probability that the theory is right under the given data NB2: Pior distributions need to applied carefully. A non-informative prior distribution is not flat (tax authorities know about that). Bayesian regularisation at each analysis step in order to avoid overfitting and select models with good generalisation properties is essential!

Individualized Classifcation & Density Forecasts

Ø  Classification Ø  Target will be true / false

Ø  NeuroBayes delivers a number that can be interpreted as a Bayesian Posterior Probability

Ø  Probability density forecast Ø  Individiual PDF for each event with asymetric uncertainties

Solution: ►  Provision of item sales predictions on a daily basis ►  Predictions for calculation of the return quota ►  Creation of detailed merchandise planning

suggestions

Retail Optimization of item sales predictions

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Result: Improvement of predictions by 40%

Inventory improvement in the double-digit million € range per year.

"A self-learning system such as NeuroBayes suits our dynamic business model Our prediction quality is increasing constantly and the sales quantities predicted are becoming ever more precise. The solution helps us adjust early on to future developments.“ Michael Sinn, Director Purchasing Support

26.09.13 Blue Yonder at BASF

Per item:

»  Sales forecast

»  Two estimates on spread (68% and 95% confidence intervals)

Sales Forecast Fashion Example: OTTO Group

Sales [units]

ROI calculations for the Otto Group

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Around 7% of all perishable foods (e.g. meat, fruit & veg., etc) have to be disposed of in German supermarkets. That‘s about 89M tons of food wasted per year…

Perishable goods in Supermarkets Meat, fruit & veg, bread, diary, ….

26.09.13 Blue Yonder at BASF

Grocery Chain: Auto Replenishment Predictions from Blue Yonder vs. In-House solution

Blue Yonder Forecasted (Actual) In-House Solution Forecast (Actual)

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Overconfidence and gut feeling produced up to 40% higher write-offs in stores not fully automated by

Blue Yonder

e.g. Individual risk predictions for car insurances: Accident probability Claims distribution Large claim prediction Contract cancellation prediction è Successfully implemented at

Correlations to target variable „Ramler II-Plot“

Alter Tarif NeuroBayes®

Premium volume

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NeuroBayes® delivers precise prognoses for the customer-individual

number and height of claims

Customer structure optimisation Bind your “good“ customers and take the „bad“ customers

Rentability improvement: Simultaneously increase your total premium volume and decrease your claims rate with a more just tariff system

Premium differentiation: NeuroBayes® adjusts premium to customer-individual risk

Alter Tarif NeuroBayes®

Claims rate

Bisheriger Tarif

Ris

iko

Private health insurance claims per year anything but normally distributed...

NeuroBayes® has the solution for difficult distributions of type

Many insured persons (fraction1-P) do not generate any claim

€

f (t) = (1− P)⋅ δ(t) + P⋅ f (t | t > 0)

When there is at least one claim, (fraction P), these are distributed according to f(t|t>0). This distribution has “fat tails“ (extremely high claims).

Difficult to handle by classical methods

t

f (t | ) = (1−P( )) ⋅δ(t)+P( ) ⋅ f (t | t > 0, )

NeuroBayes® calculates for each insured person x the individualised Bayesian probability density.

Insured person x will have no claims

with probability 1-P(x)

If insured person x will have any claim, the costs will be distributed according

to f(t|t>0,x)

t

x x x x

δ(t) = Dirac- delta- ,,function‘‘ (distribution)

NeuroBayes® has the solution for difficult distributions of type

Healthcare insurance – long term prediction from anamnesis

NeuroBayes® Expert Estimation (risk premium loading)

Ø  Expert estimations are at best random – for patients with a long history even systematically wrong.

Ø  NeuroBayes® forecasts costs correctly and significantly beats expert estimations more than 10 years into the future

Revenue Forecast Example: dm– Large German drug-store chain

Key Challenge:

»  Revenue prediction for each individual store,

»  Used for staff planning

»  Up to ½ year in advance

»  Keep track of opening times, public holidays, weather,..

02.1SalesForecast

=∑∑

Easter Ascension Whitsun

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Forecasts for individual stores »  Prediction of the full probability

density function. »  Precise forecast of the exptected

revenue including exptected spread (68% and 95% confidence intervals)

Revenue Forecast Example: dm– Large German drug-store chain

revenue

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Further Examples

»  Churn-Management in telecommunications

»  Identify customers who have a high risk to cancel their monthly contract

»  Forecast of targeted promotions and individual measures to prevent churn.

»  Churn-Management for daily newspapers

»  Identify 67% of all customers likely to cancel their contract by predicting the “most interesting” 10% of all customers to target.

„Blue Yonder beats all our churn prediction models. The more complicated and challenging the task the better. NeuroBayes® outperforms the competition.“

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Further Examples

Risk-Management

Among all customers who have a high risk of not paying their debts, identify those who are most likely to pay their outstanding debts.

Many more

0 €

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200.000 €

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300.000 €

35 Tage 60 Tage

Testgruppe Kontrastgruppe NeuroBayes Conv. approach

days days

Prognosis of sports events from historical data: NeuroNetzer

Results: Probabilities for home - tie – guest

Blue Yonder: Awards for Big Science Startup

3 time winner of the Data Mining Cup

Retail Technology Award 2012

bwcon Hightech

Award 2012

Special Prize Deutsche Boerse

2012

DLD 2013: Best Enterprise Solution

Finalist 2012 Finalist 2013

Disclaimer

26.09.13 Page 44 Blue Yonder at BASF

This Presentation (the Presentation) has been prepared by Blue Yonder GmbH & Co KG (collectively, with any officer, director, employee, advisor or agent of any of them, the Preparers) for the purpose of setting out certain confidential information in respect of Blue Yonder’s business activities and strategy. References to the “Presentation” includes any information which has been or may be supplied in writing or orally in connection with the Presentation or in connection with any further inquiries in respect of the Presentation. This Presentation is for the exclusive use of the recipients to whom it is addressed. This Presentation and the information contained herein is confidential. In addition to the terms of any confidentiality undertaking that a recipient may have entered into with Blue Yonder, by its acceptance of the Presentation, each recipient agrees that it will not, and it will procure that each of its agents, representatives, advisors, directors or employees (collectively, Representatives), will not, and will not permit any third party to, copy, reproduce or distribute to others this Presentation, in whole or in part, at any time without the prior written consent of Blue Yonder, and that it will keep confidential all information contained herein not already in the public domain and will use this Presentation for the sole purpose of setting out [familiarizing itself with] certain limited background information concerning Blue Yonder and its business strategy and activities. The foregoing confidentiality obligation shall be legally binding for the recipient infinitely. This Presentation is not intended to serve as basis for any investment decision. If a recipient has signed a confidentiality undertaking with Blue Yonder, this Presentation also constitutes Confidential Information for the purposes of such undertaking. While the information contained in this Presentation is believed to be accurate, the Preparers have not conducted any investigation with respect to such information. The Preparers expressly disclaim any and all liability for representations or warranties, expressed or implied, contained in, or for omissions from, this Presentation or any other written or oral communication transmitted to any interested party in connection with this Presentation so far as is permitted by law. In particular, but without limitation, no representation or warranty is given as to the achievement or reasonableness of, and no reliance should be placed on, any projections, estimates, forecasts, analyses or forward looking statements contained in this Presentation which involve by their nature a number of risks, uncertainties or assumptions that could cause actual results or events to differ materially from those expressed or implied in this Presentation. Only those particular representations and warranties which may be made in a definitive written agreement, when and if one is executed, and subject to such limitations and restrictions as may be specified in such agreement, shall have any legal effect. 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This Presentation shall remain the property of Blue Yonder. Blue Yonder may, at any time, request any recipient, or its Representatives, shall promptly deliver to Blue Yonder or, if directed in writing by Blue Yonder, destroy all confidential information relating to this Presentation received in written, electronic or other tangible form whatsoever, including without limitation all copies, reproductions, computer diskettes or written materials which contain such confidential information. At such time, all other notes, analyses or compilations constituting or containing confidential information in the recipient’s, or their Representatives’, possession shall be destroyed. Such destruction shall be certified to Blue Yonder by the recipient in writing. Neither the dissemination of this Presentation nor any part of its contents is to be taken as any form of commitment on the part of the Preparers or any of their respective affiliates to enter any contract or otherwise create any legally binding obligation or commitment. The Preparers expressly reserve the right, in their absolute discretion, without prior notice and without any liability to any recipient to terminate discussions with any recipient or any other parties. The distribution of this Presentation in certain jurisdictions may be restricted by law and, accordingly, recipients of this Presentation represent that they are able to receive this Presentation without contravention of any unfulfilled registration requirements or other legal restrictions in the jurisdiction in which they reside or conduct business.