Predictive analytics The next generation
Rok Okorn, Ektimo d.o.o.
About predictive analytics
Supervised, unsupervised or reinforcement
Examples of usage
What is data science?
Data science reveals previously unknown cause and effect
relationships and possibly forecasts future events by a
systematic analysis (of large amounts) of data.
Objective: usage of data for improved business cases.
New levels of analytics with a data science
Why it happened?
What will happen?
Which decision leads
to the best outcome?
Big data use cases 2015,
perform big data
increase in income
due to big data
reduction due to
big data projects.
In the center of data
science is artificial
These algorithms enable
• learn from past data without
• improve with new data.
• effectively recognize patterns in
complex data from a variety of
Supervised, unsupervised or reinforcement?
Example: object recognition
Learn by examples as to what object it is in terms of structure, color, shape, etc. So
that after several iterations it learns to define an object.
There is no desired output that is provided, therefore categorization is done so that
the algorithm differentiates correctly between bikes, cars, houses or people
(clustering of data).
The predictions are continuously updated, unlike in the previous types. For
example, when a robot sees an object: first classify it and then go around it and
classify it again on new observed parameters. Alternatively, when the robot learns
that some object is dangerous, it will avoid it, next time
What we knew about person A
up to some date T?
1 month after T?
301.2 1 4.5 1 10.9
Person A’s digital print
The model predicts a
purchase for person X
with 67% probability
Ensembles – diversification at the level of models
Final prediction based on
some function of the
individual models, e.g. mean
Instead of one single model we train multiple different models.
Variouos use cases
• Demand forecasting
• Loyalty programs
• Dynamic pricing
• Recommendation systems
• Optization of asortment
• Credit scoring
• Claims prediction
• Fraud detection
• Predictive lead scoring
• Susceptibility to the purchase
• Churn prediction
• Customer lifetime value prediction
• Routing optimization
Usage of PA in mobility
What elementary problems need to be solved?
• Basic infrastructure
• Data gathering
• What are the KPIs?
Predictive analytics tasks:
• Predict (stochastic) demand and supply
• Predict defects, malfunctions or failures
• Recognize objects on paths and deal with them
Image recognition – problem formulation
•What is it?
Handwriting, CAPTCHAs; discriminating humans from
•Where is it?
Detecting objects regions in images
•How is it constructed?
Determining how a group of something is related (e.g. math
symbols) or determining some structure of objects
Given a database of objects and an image
determine what, if any of the objects are
present in the image.
Image recognition – solution I
source: Bernd Heisele,Visual Object Recognition with Supervised Learning
Image recognition – solution II
Demand prediction - problem formulation
Different forecasts for different types of products:
• Nondurable consumer goods
• vanish after a single act of consumption
• depends upon price of the commodity and the related goods and population
• Durable consumer goods
• can be consumed a number of times or repeatedly used
• depends upon social status, level of money income, taste and fashion, the
provision of allied services and their cost, sensitive to price changes
• Capital goods
• used for further production
• depends on the specific markets they serve and the end uses for which they are
bought, consumption per unit of each end-use product
• new to the consumers
• depends on type (evolution, substitute), same group products demand
and past data,
demand of a
Demand prediction – solution I
Classical time series approach
• ARIMA, GARCH
Demand prediction – solution II
Machine learning methods
source: Application of machine learning techniques for supply chain demand forecasting Original Research Article,
European Journal of Operational Research, Volume 184, Issue 3, 1 February 2008
Demand prediction– mobility usage
• Predicting demand in a specific location
• Adding new infrastructure elements (stations, cars)
• Dynamic pricing
• Power demand
Predictive maintenance - problem formulation
Can you tell
me, when to
Three types of maintenance:
• emergency; when failure occurs
• preventive; regularly on time, cleaning cycle of x weeks
• predictive; when it is needed
Predictive maintenance is condition based using advanced
technology and instrumentation
Assumes installed indicators; read and reported by operators or
•What symptoms indicate the pending failure under review?
•How can the symptom be detected?
•Which methods of detection might be useful?
•How long is the anticipated failure development period?
•What does this suggest about inspection intervals?
Predictive maintenance – solution I
source: Architecture diagram: Solution Template for predictive maintenance
Predictive maintenance – solution II
gathering and labeling data, problem
• Image recognition
range of viewing conditions, 2D vs. 3D, point
of view, size of known image pool
• Demand prediction
seasonality, special events, weather,
location, only sales data (instead of
• Preventive maintenance
immediate critical faults, sensor placements