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How to pronounce two-time in Dutch?

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while they're changing places i will
i will introduce the next speaker which
is boris kuda
boris is an assistant professor at csai
he has a phd in computer science from
the university of antwerp he was also a
postdoc there for a while
he is a rather popular lecturer in our
master data science and society in
particular the course on big data
analytics is one that is highly
appreciated by students
his research focuses is on sequential
pattern mining
particular um
where so where he
introduces novel measures to evaluate
patterns
he also worked on a broadly relevant
topic such as anomaly detection in time
series
discovering anomalous patterns is
in a complex heterogeneous time series
data
is interesting as a has a high range of
applications but this is not an easy
task to give an example of the
difficulty an outline
outlier in such a data set can represent
an important source of information but
can also be rather spurious or
irrelevant for the task at hand
today boris will talk about
pattern-based time series classification
but norris
force yours
so good afternoon everyone welcome to my
talk
as peter said uh i will be talking about
a pattern-based time series
classification
uh that's quite a long title uh full of
uh terminology that i will now try to
unpack so i will start off with a with a
general introduction about time series
data
what classification is and a little bit
about pattern mining before describing
how it all
comes together this presentation is
largely based on a recent publication of
mine
together with a couple of colleagues
from the university of antwerp
so let's get going with some basics
first of all
what is time series data time series
data is
defined in a in a fairly simple way as
an ordered sequence of values typically
real numbers
which come
at regular temporal intervals so that's
a
fairly broad definition of what a time
series is
as peter said time series are
encountered
in a variety of settings
all over the with a variety of
applications
uh in in the real world so
for example uh sensor readings whether
that be uh weather data temperature
pressure
uh whatever else sensors could be
measuring uh gps data trajectories uh
motion sensors
but also
other types of data such as medical data
stock exchange data
etc so
i will be talking today
mostly in fairly generic terms
uh describing
methodology and algorithms rather than
talking about a particular application
but
what i will be presenting
uh has a wide
variety of application and possibilities
to be to be applied
in a variety of settings
there are different types of time series
we call them the three different use
cases when we talk about time series
the first is a simple case the
univariate time series
where your data consists only of a
single sequence of values
so for example a single
stock price
where your data set could then contain
multiple stock prices which are each
considered a univariate time series
a more complicated problem setting is
where your data consists of multivariate
time series
where you have
some several time series linked together
and forming some sort of entity
so for example uh if you have a machine
which
which is connected to several sensors
and those sensors measure
all kinds of information about that
machine like i said it could be the air
temperature it could be the
the temperature of the machine itself it
could be some kind of
throughput or energy generation or
energy consumption
so all kinds of things that are measured
about the same machine at the same time
at the same regular intervals that way
we obtain a multivariate time series
and finally
the most complex uh
type of
data that we could have is the so-called
mixed type
time series where in fact
you don't only have a time series as
i've defined it on the previous slide so
you have uni or multivariate time series
but you are coupling them to some event
log data and again event log data is
something very generically defined as a
sequence of events
and this sequence of events
doesn't necessarily have to contain
let's say real valued measurements
doesn't even have to contain numerical
data
and doesn't even have to come at regular
time intervals
so that adds to the complexity of the
problem
an example of this is again you have a
machine
you have some sensor data being
collected about that machine but you
also have an event log a log of for
example
what a machine operator does with that
machine which can happen
at all kinds of irregular
time intervals
uh for example uh
why is this relevant well this this this
information of course can inform us
about the time series data and inform us
about
how normal or abnormal the time series
data is and can add to the patterns that
we can find in the time series to give a
trivial example if
you are measuring the output of a
particular machine and all of a sudden
the output drops to zero
you might think there is something wrong
but if you couple this with an event log
data which tells you that the machine
operator at that moment switched off the
machine then of course this is perfectly
normal behavior and by
combining these two sources of
information
uh we are perhaps able to gain insights
that we otherwise wouldn't be able to do
the next
bit of terminology that i have in the
title of the talk is classification this
is again a fairly
simple concept that is perhaps known to
most of the audience
so i will just very very quickly sum up
what we mean by classification
essentially
classification is the process of
learning from a training set and
applying this
knowledge what you've learned on the
training set to classifying new
instances of data
i give here
a nice example let's say not not
directly a time series example
uh an example of image classification
where we have images of
four different classes of animals in
this case
we have beetles we have flies we have
birds and we have chickens
so
from the small training set that is
there we could try to learn how to
classify new instances and when a new
instance comes in
we
want to assign a class label to that new
instance based on
what we have learned from the training
set
now of course how does this tie in with
classified time series well we can in
the same way
classify time series
of course the problem becomes a little
bit more complex because time series
come in all sorts of shapes and forms
and even time series of
the same class as we can see on this
example
can be
quite uh varied in comparison to each
other
so what is the goal of time series
classification what is the goal of
learning
uh what do we want to learn from a
training set in time series
classification
well we want to learn two things
what makes time series of a particular
class similar to each other that will
allow us to assign a particular class
label to
another new time series that is
potentially similar to
known time series of a particular class
but also what makes time series of a
particular class different from those of
other classes
so again
to give a simple example
if you find
a particular feature
that is present in all time series of a
particular class
that feature might be useful for
classification but it might not be
if that feature is also present in time
series of other classes then that
feature is not very useful for
classification
so we try to find
frequent
patterns as we will see
in different classes of time series we
try to see if patterns are good for
discriminating between classes and which
patterns are not and those ones we don't
use for the classification task
so how do we do time series
classification where well there are two
approaches and two types of algorithm
in general
you can compare time series directly to
each other so you can feed the time
series into an algorithm
and the algorithm then produces a
classifier
or
we can try to transform the time series
into feature vectors that
somehow describe the time series
and that way
we can then compare those feature
vectors vectors to each other and in
that way learn
a classifier
here is a small example again where we
see
i think
two time series from eight different
classes each one each class of a
different color
uh
they are sequentially ordered so the
first two are from the same class the
sec the third and fourth are from the
second class etc
we can see the original time series on
the left
and we can also see what happens if we
simply
apply the nearest neighbor classifier on
the road time series data
and we can see that it goes wrong even
though visually these time series are
quite clearly similar to each other the
ones that are in the same class uh a
nearest neighbor classifier would
struggle with this if you just use
euclidean distance point by point for
example
so this is in itself not a good idea
so what we do that's the middle column
there and on the right column you can
see what another method does which does
similar thing to what we do
uh so what we do is we transform the
time series into feature vectors
and then we compare the feature vectors
to each other and we can you can see
here on these uh plots
that the feature vectors actually of the
two time series that belong to the same
class are always
quite similar to each other and are in
fact in this very very trivial example
always
the first nearest neighbor of each other
so how do we do this well we do this by
mining patterns in time series and what
are patterns well we look for
reoccurring sequential patterns in time
series data so we take the time series
data we find reoccurring patterns in
them
and then
we want to use those patterns to answer
those two questions that i've asked a
few slides ago
so the intuition behind our method is
that if a new instance
a new time series contains the same
patterns and as known time series of
particular class then the new instance
has a high probability to belong to that
class
if the instance doesn't contain the same
patterns and as
other known time series of a particular
class then it's less likely to belong to
that class
so we aim to discover discriminative
patterns for each class
and then we classify new instances
by essentially checking whether the new
time series
has known patterns
of
all the classes and
whether it lacks the known patterns of
all the classes and by doing that we can
compute
to which class
the new
instance most likely belongs to
how do we build the feature vectors
well essentially each pattern that we
mine
that is discriminative that we decided
to keep
we use it as a feature
so the embedding of a time c of of time
series data into feature space
essentially produces a matrix in this
matrix each time series is a row and
each pattern is a column
and then we try to see which patterns
are found in
which time series
we assign
if we if we look for exact pattern
matching we assign one if the pattern is
present in time series and a zero
otherwise
uh we also
experimented a little bit with an
alternative method which used
approximate pattern matching where we
defined the similarity between each
pattern and its nearest occurrence in
the time series
in in all these examples and during the
talk i'm talking about
i'm giving examples of univariate time
series but everything that i've
theoretically mentioned can also be used
for a
multivariate and
mixed type time series a word about
interpretability of our method which is
also important
in classification so that users of
algorithm also get some
feedback on why a particular
classification took place i'm giving
here back the example of
beetles and flies
an interesting example because it also
shows that
what is at first glance not a time
series can be
turned into a time series and then
of course you can utilize time series
classification methods that you would
otherwise might not be able to do
so what we do here is we take the images
we take their outlines and we convert
them into time series by essentially
measuring the distance from each
each point on the outline to the center
point of the image
and that way we obtain time series by
essentially rotating
along the image outline
an interesting note you can also see
here that when we talk about a time
series the the actual x-axis doesn't
have to represent
time
so we did this on this example data set
we classified the time series and we can
convert them back in to images doing the
same way
and what we can do
when i say producing feedback we can see
here on the visualization
uh we have two classes of objects
beetles and flies in this case
and we have actually visualized patterns
that we have associated with beetles in
blue and patterns that we have
associated with flies in red
and we can see that in this small data
set of 20
animals 20 insects we actually got three
of them wrong and we can see exactly why
we got them wrong so in the first row
the second image we can see that we
classified it as a fly when it's in fact
a beetle
and highlighted in red on that image
are the patterns that we found in that
image that are normally associated with
flies
so by analyzing
the results of the classification in
this way you can get insights that you
otherwise might not be able to
obtain
a very quick word on the evaluation
because i'm running out of time so we
have compared
our classifier
with
tens of other classifiers on hundreds of
data sets
and of course all the evaluation that we
can do we can talk about
uh average results
uh
what was for us important was the
interpretability that was our main
motivator for this work we have a
classifier that's fully explainable
we also have a classifier that's
applicable to all three use cases that i
mentioned so univariate multivariate and
mixed type data
which
for which there are not a lot of other
classifiers available
we have a relatively fast method in fact
fast faster than most existing
algorithms because it's
relatively simple and based on
pattern mining
and finally
we have a
classifier that produces very good
accuracy results so like i said we're
talking about averages here to our
surprise
uh
our method didn't do much worse than the
best
deep learning methods it did do slightly
worse i say narrowly outperformed
but we did
test on hundreds of data set and in fact
in about a third of them our method
outperformed the deep learning methods
which
which was quite we were quite positively
surprised by that
and our method produced higher accuracy
than any other pattern based method
so to summarize i give a very small
summary of how the method works on
univariate multivariate and mixed type
time series and i think my time is up
with that so thanks for your attention
i'm happy to answer any questions thank
you
there is one question uh in the chat uh
actually too already so that's that's
pick one so can you elaborate a little
on the feature factors vectors
how is a certain pattern chosen to be
put in the matrix
okay
so
first of all we have
what i said on this slide
we are looking for discriminative
patterns for each class so
we mine
all frequent patterns
and then we see which ones are
discriminative like i said at the
previous example
uh
patterns that are present in all classes
are not particularly useful for
classification but patterns that are
present in a single class and not
present in other classes are so that's
what we mean by discriminative patterns
that's how
patterns are selected
uh
and
then
we have a uh essentially an embedding
then we have we know which patterns
represent the columns in that matrix
and then we look at a row by row basis
so we are looking at the time series
that we have
and we check whether those patterns are
present in a time series or not
i don't know if it was the scope of the
question uh how do i fill in the matrix
in other words where the values come
from so like i said we have we have
tested actually a number of um
variants of our algorithm but i didn't
have time to go into that
one variant worked with exact pattern
matching so
purely binary the value would be one if
the pattern is present in the time
series the value would be zero if the
pres if the pattern is not present
we've also experimented with with
approximate pattern matching where we
said
for each pattern we try to find the best
match in the time series we then measure
the similarity between the pattern and
the best match
and then we assign that value so then
the the vectors are not no longer binary
but the vectors actually uh contain
values between zero and one
so real values between zero and one the
higher the value the better the match
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you

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two-time::verb | Spocien Dictionary

two-time

verb

/tu_taɪm/

carry on a romantic relationship with two people at the same time

Type	Words
Type of	betray, cheat, cheat on, cuckold, wander

How to pronounce two-time in Dutch?

two-time::verb | Spocien Dictionary

abandonment

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