Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
17,
No.
2,
September
2020,
pp.
1602
1609
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v17i2.pp1602-1609
r
1602
A
computing
model
f
or
tr
end
analysis
in
stock
data
str
eam
classification
Abdul
Razak
M.
S
1
,
Nirmala
C.
R
2
1
Department
of
Computer
Science
and
Engineering,
Bapuji
Institute
of
Engineering
and
T
echnology
,
India
2
V
isv
esv
araya
T
echnological
Uni
v
ersity
,
India
Article
Inf
o
Article
history:
Recei
v
ed
Sep
28,
2019
Re
vised
Mar
13,
2020
Accepted
Apr
8,
2020
K
eyw
ords:
Classification
Data
stream
Stock
trading
T
rend
analysis
Data
stream
ABSTRA
CT
F
or
se
v
eral
decades,
man
y
statistical
and
scientific
ef
forts
took
place
for
the
better
analysis
or
prediction
of
stock
trading.
But
still
it
is
open
to
of
fer
ne
w
a
v
enues
for
the
scientists
to
rethink
and
disco
v
er
ne
w
inferences
by
adopting
latest
technological
scenarios.
In
this
re
g
ard,
we
are
trying
to
apply
classification
techniques
on
stock
data
stream
through
feature
e
xtraction
for
the
trend
analysis.
The
proposed
w
ork
is
in
v
olv-
ing
k-means
for
clustering
samples
into
tw
o
clusters
(the
stocks
in
trend
as
one
cluster
and
another
on
as
stocks
not
in
trend).
The
trend
analysis
is
done
based
on
density
esti-
mation
of
the
stocks
with
respect
to
sectors.
A
well-kno
wn
data
representation
method
that
is
histogram
is
used
to
represent
the
sector
whic
h
is
in
trend.
This
w
ork
has
been
implemented
and
e
xperimented
by
considering
li
v
e
NSE
(india)
data
using
p
ython
and
its
related
tools.
Copyright
c
202x
Insitute
of
Advanced
Engineeering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Abdul
Razak
M.
S,
Department
of
Computer
Science
and
Engineering,
Bapuji
Institute
of
Engineering
and
T
echnology
,
Da
v
angere,
India.
Email:
msabdulrazak@gmail.com
1.
INTR
ODUCTION
Data
stream
analysis
has
opened
up
ne
w
a
v
enues
or
opportunities
for
Computer
Science
and
Engi-
neering
Scienti
sts.
The
data
stream
is
a
recorded
data
with
respect
to
time,
perhaps
it
can
be
re
g
arded
as
signal.
Sometimes
the
signal
may
be
continuous
or
discrete.
All
the
parameters
apply
to
s
ignals
holds
good
for
data
streams.
Stock
trading
and
its
transactions
can
generate
numerous
amount
of
data
with
respect
to
time
and
hence
it
can
be
re
g
arded
as
a
data
stream.
Data
stream
classification
[1]
is
an
area,
enables
researchers
to
iden-
tify
or
e
xtract
ne
w
features
through
an
y
acceptable
scientifi
c
process.
Classification
techniques
on
stock
data
analysis
may
pro
vide
certain
inferences.
One
such
inference
could
be
trend
of
the
stock.
Indian
Stock
mark
et
has
identified
ele
v
en
major
sectors
[2]
to
cate
gorize
the
stocks.
T
rend
analysis
[3]
is
the
process
of
estimating
the
entity
which
is
in
tre
n
d
or
has
grabbed
attention
among
the
participating
entities.
Some
of
the
stocks
may
be
in
trend
due
to
se
v
eral
reasons
that
is
season,
price,
need,
dependenc
y
,
alternate
a
v
ailability
,
price
do
wn
in
je
welery
,
currenc
y
mark
et
and
so
on.
Data
stream
analysis
[4]
is
one
of
the
most
challenging
process
in
internet
applications.
Due
to
its
continuous
a
v
ailability
and
updations
an
ef
ficient
techniques
to
process
and
declare
inferences
are
required.
Stock
mark
et
[5]
produces
enormous
amount
of
data
in
the
repository
.
The
analysis,
management
of
ab
undant
data
and
producing
acceptable
results
is
one
of
the
biggest
challenge
[6]
to
the
computer
scientists
because
the
beha
vior
of
the
system
v
aries
as
the
ne
w
data
is
added
to
repository
.
Classification
model
may
pro
vide
certain
re
v
olutionary
inferences,
perhaps
classification
on
data
stream
may
tend
to
lot
of
openings
with
respect
to
performance
of
the
classification
model.
Classifica-
tion
models
be
gins
with
feature
e
xtraction
that
is
p
r
op
e
rties
which
may
define
samples
as
per
the
analysis.
J
ournal
homepage:
http://ijeecs.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
1603
The
process
of
feature
e
xtraction
on
data
stream
and
classification
of
these
features
has
cre
ated
number
of
a
v
enues
in
the
research
domain.
In
this
paper
,
we
mainly
focus
on
the
estimation
of
the
sector
(collection
of
stocks
belongs
to
similar
properties),
which
is
in
trend
for
a
gi
v
en
time
period
through
class
ification
techniques.
Features
are
e
xtracted
by
considering
li
v
e
data
from
the
NSE
serv
er
and
clustered.
The
proposed
w
ork
has
conducted
e
xperiments
using
AN
A
COND
A
[7]
and
Jup
yter
tools
by
in
v
olving
nsep
y
[8]
p
ython
package
for
li
v
e
data.
2.
REVIEW
OF
LITERA
TURE
Since,
be
ginning
of
the
stock
trading
se
v
eral
e
xperiments
are
in
progress
to
in
v
ent
required
decla-
rations.
Author
[9]
discuss
the
v
olatility
of
K
uala
Lampur
Composite
Inde
x
using
stochastic
v
olatility
(SV)
models
and
Generalized
Auto
re
gressi
v
e
conditional
heteroscedasticity
(GARCH)
models.
The
model
results
pro
v
e
the
slight
dif
ferences
in
Root
Mean
Squared
error
and
Mo
ving
A
v
erage
En
v
elope.
T
otally
971
daily
observ
ations
of
KLCI
Closing
price
inde
x,
from
2nd
January
2008
to
10th
No
v
ember
2016,
e
xcluding
public
holidays.
SV
model
is
found
to
be
the
best
based
on
the
lo
west
RMSE
and
MAE
v
alues.
Author
[10]
in
v
olv
es
study
of
financial
mark
et
for
fi
v
e
dif
ferent
companies
from
Malaysia
namely
CIMB,
Sime
Darby
,
Axiata,
Maybank
and
Petronas
using
Machine
Learning
Algorithms.
T
w
o
types
of
e
xperiments
were
conducted
based
on
the
type
of
data.
The
first
e
xperiment
used
te
xtual
data
using
financial
ne
ws
in
v
olving
6368
articles
and
classified
as
positi
v
e
or
ne
g
ati
v
e
using
SVM.
The
second
e
xperiment
used
numeric
historical
data
in
v
olving
5321
records
to
predict
the
stock
price
is
going
up
or
do
wn
using
Random
F
orest
algorithm.
Author
[11]
has
tried
to
propose
an
embedded
st
reaming
SVM
classification
architecture
for
continuous
data
processing.
P
aper
[12]
presents
dynamic
w
ay
of
selecting
the
number
of
clusters
in
K-means
clustering
algorithm.
The
proposed
algorithm
is
applied
for
clustering
iris
datas
et
and
the
performance
of
the
algorithm
is
mea-
sured
using
inter
cluster
distance
and
sum
of
squared
error
parameters
and
compared
with
General
K-Means
algorithm.
Author
[13]
has
proposed
a
classification
model
to
answer
comple
x
question
answering
process.
Author
[14]
presents
the
ef
fects
of
ne
ws
in
online
social
media
ef
fects
purchase
of
pharmaceutica
l
stocks.
The
e
xperiment
is
conducted
using
Nifty
pharma
inde
x
data
and
de
v
eloped
sentiment
analysis
model
for
pharma
stock
prediction.
The
sentiment
analysis
model
achie
v
ed
an
accurac
y
of
70.59%
in
predicting
daily
stock
mo
v
ement.
Author
[15]
presents
analysis
and
prediction
of
US
real
time
stocks
data
from
yahoo
finance
using
big
data
analytics.
A
machine
learning
model
is
de
v
eloped
to
predict
the
future
crude
oil
price
using
the
United
States
Oil
fund
(USO)
data
.
The
model
identifies
the
be
st
features
for
better
oil
price
prediction.
In
paper
[16]
presents
an
analysis
of
one
year
US
stock
mark
et
based
on
Netw
ork
approach.
The
paper
ad-
dresses
the
correlation
of
one
stock
with
other
stocks
and
also
identifies
the
k
e
y
players
in
the
mark
et
based
on
their
number
of
dependencies.
Author
[17]
addresses
the
selection
of
stock
using
both
technical
and
fundamental
infor
mation.
A
frame
w
ork
is
designed
to
mak
e
class
predictions
for
the
industrial
sector
of
the
Australian
stock
mark
et.
The
stock
selection,
trading
s
trate
gy
outperformed
the
Australian
stock
inde
x.
The
accurac
y
of
the
classifica-
tion
models
lik
e
Decision
tree,
CHAID
tree
and
Neural
netw
ork
is
compared.
3.
METHODOLOGY
Figure
1
depicts
the
methodology
of
the
proposed
research
w
ork.
3.1.
Data
collection
Classification
w
ould
result
most
useful
inferences,
these
inferences
mainly
depend
on
the
appli
cable
data
which
is
collected
from
the
en
vironment.
This
w
ork
requires
a
data
stream
that
is
the
li
v
e
data,
which
is
continuous
and
may
f
all
within
some
range.
The
range
of
data
from
start
time
to
end
time
decides
the
stock
and
its
trend
in
the
mark
et.
nsep
y
is
the
p
ython
package
used
to
access
li
v
e
NS
E
India
stock
trading
data.
This
package
pro
vides
the
parameters
of
each
stock
namely
totalT
radedV
olume,
totalT
radedV
alue,
Open,
Close,
dayHigh,
dayLo
w
and
so
on.
3.2.
F
eatur
e
extraction
The
proposed
methodology
is
considered
the
stock
data,
which
is
with
respect
to
time
as
a
di
screte
signal.
Hence
all
the
applicable
features
corresponding
to
discrete
signals
are
considered
as
features
in
the
proposed
w
ork.
In-spite
of
man
y
features,
only
features
are
considered
as
per
the
analysis
and
which
gi
v
es
A
computing
model
for
tr
end
analysis
in
stoc
k
data
str
eam
classification
(Mr
.
Razak)
Evaluation Warning : The document was created with Spire.PDF for Python.
1604
r
ISSN:
2502-4752
better
results
and
this
process
is
called
as
feature
selection
[18].
The
range
of
data
from
start
time
to
end
time
decides
the
stock
and
its
trend
in
the
mark
et.
In
order
to
achie
v
e
better
analysis
the
type
of
data
and
its
importance
does
matter
in
the
classification
and
conclusion.
The
importance
and
its
type
can
be
found
based
on
e
xperience
of
the
stock
trading
or
through
computing
analysis.
Stock
data
ha
v
e
se
v
eral
parameters,
namely
totalT
radedV
olume,
totalT
radedV
alue,
Open,
Close,
dayHigh,
dayLo
w
and
so
on.
Theses
parameters
are
used
for
further
feature
e
xtraction.
3.2.1.
Standard
de
viation
Since
the
model
operates
on
the
data
which
is
with
respect
to
time,
the
amount
of
standard
de
via-
tion
[19]
within
the
members
is
essential
to
estimat
e.
This
feature
mainly
produces
the
amount
of
fluctuation
among
the
members
of
the
data
stream.
Stock
data
stream
is
a
sequence
of
v
alues
with
respect
to
time
or
date.
This
paper
has
considered
fi
v
e
properties
from
the
get
history
of
nump
y
package
of
p
ython.
These
fi
v
e
proper
-
ties
are
Open,
Close,
Lo
w
,
High
and
V
olume.
F
or
each
stock
and
each
property
Standard
De
viation
i
s
estimated.
3.2.2.
K
urtosis
¨
Kurtosis
i
s
a
mea
sure
of
the
combined
weight
of
a
distrib
ution
´
s
tai
ls
relati
v
e
to
the
center
of
the
distrib
ution
¨
.
This
measure
may
declare
the
rise
in
the
distrib
ution
if
the
measure
t
u
r
ns
to
positi
v
e
[20].
Figure
2
clearly
depicts
the
positi
vity
and
ne
g
ati
vity
nature
of
the
measure
along
with
distrib
ution
pattern.
Figure
1.
Proposed
methodology
for
trend
analysis
in
stock
data
stream
classification
Figure
2.
T
ailed
and
centered
distrib
ution
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
17,
No.
2,
September
2020
:
1602
–
1609
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
1605
3.2.3.
A
ugmented
Dick
ey-Fuller
T
est
[21]
This
feature
applies
to
Non-stationary
T
ime
v
ariant
systems.
Stock
mark
et
data
series
can
be
re
g
arded
as
non-stationary
because
the
mean
and
v
ariance
of
the
system
is
v
aried
at
an
y
point
of
time.
This
model
is
well
suitable
for
stock
mark
et
data
to
analyze
the
stock
trend.
This
paper
uses
the
unit
root
with
drift
test
analysis
of
the
Dick
e
y-Fuller
test.
Unit
root
or
stationarity
of
the
distrib
ution
can
be
estimated
using
the
(1).
Y
t
=
1
+
2
t
+
Y
t
1
+
M
X
i
=1
i
Y
t
i
+
t
:
(1)
Figure
3
depicts
t
he
data
stream
which
is
in
trend
and
the
same
with
drift.
This
paper
uses
only
deterministic
time
trend
coef
ficient
as
a
feature
for
further
classification.
Figure
3.
T
rend
analysis
with
drift
3.3.
Dimensionality
r
eduction
using
PCA
The
proposed
methodology
e
xtracting
around
twelv
e
features
that
is
three
features
from
each
property
(Open,
Close,
High,
Lo
w
,
V
olume)
of
the
stock
data.
This
process
is
defined
around
twelv
e
features,
sometimes
all
these
features
may
or
may
not
play
an
important
role
in
the
classification.
Hence,
dimensionality
reduction
[22]
is
one
of
the
techniques
to
reduce
the
number
of
features.
This
may
reduce
the
comple
xity
of
the
classi-
fication
and
may
impro
v
e
the
process
better
with
meaningful
inferences
[23].
Principal
Component
Analysis
(PCA)
is
one
of
the
readily
a
v
ailable
a
lgorithms
for
Dimensionality
Reduction.
The
proposed
w
ork
reduces
the
twelv
e
features
to
three
features.
3.4.
Clustering
using
K-means
clustering
algorithm
The
proposed
w
ork
is
grouping
the
a
v
ailable
stock
features
into
tw
o
clusters
using
k-means
clus
tering
[24]
as
sho
wn
in
Figure
4.
Where
one
will
be
containing
the
stocks
which
are
in
trend
and
another
not.
3.5.
T
r
end
analysis
This
is
the
final
phase
of
the
methodology
,
which
considers
all
the
samples
from
cluster
2
(Cluster
2
is
assumed
as
trend
cluster
,
it
contains
all
the
samples
whose
features
ha
v
e
gi
v
en
trend
coef
ficients).
Distance
from
origi
n
to
the
centroid
of
the
cluster
declares
the
selection
of
the
cluster
which
has
stocks
in
trend.
More
the
distance
more
will
be
the
trend,
this
assumption
is
based
on
trial
and
error
method.
Apply
histogram
on
stock
cate
gory
(stock
indices)
of
the
samples.
As
per
the
surv
e
y
,
there
are
ele
v
en
stock
indices
in
Indian
Stock
mark
et.
Figure
5
depicts
a
sample
histogram,
which
declares
that
banking
sector
inde
x
is
in
trend
compared
to
all
other
sectors.
The
histogram
[25]
is
clearly
depicting
the
s
tatus
of
the
sectors
in
the
mark
et.
This
status
indicates
that
the
sector
number
5
is
in
trend.
This
trend
may
change
as
per
the
mark
et
transactions.
A
computing
model
for
tr
end
analysis
in
stoc
k
data
str
eam
classification
(Mr
.
Razak)
Evaluation Warning : The document was created with Spire.PDF for Python.
1606
r
ISSN:
2502-4752
Figure
4.
Samples
cate
gorized
into
tw
o
clusters
Figure
5.
Histogram
based
on
sector
indices
of
samples
4.
RESUL
TS
AND
DISCUSSIONS
As
per
NSE
(National
Stock
Exchange)
Nifty
Auto
Inde
x,
Nifty
Bank
Inde
x,
Nifty
Financial
Ser
-
vices
Inde
x,
Nifty
FMCG
Inde
x,
Nifty
IT
Inde
x,
Nifty
Media
Inde
x,
Nifty
Pharma
I
nd
e
x,
Nifty
Pri
v
ate
Bank
Inde
x,
Nifty
PSU
Bank
Inde
x,
Nifty
Realty
Inde
x,
Nifty500
Industry
Indices
are
the
ele
v
en
sector
indices.
In
the
propose
d
w
ork,
fifteen
stocks
ha
v
e
been
considered
in
each
sector
inde
x
for
the
trend
analysis.
Figure
6
sho
ws
the
feature
v
alues,
e
xtracted
from
the
selected
parameters
(Open,
Close,
Lo
w
,
High
and
V
olume)
within
a
gi
v
en
period.
Figure
6.
Feature
v
alues
of
selected
stocks
from
sectors
Figure
7
sho
ws
the
results
from
PCA
(dimensionality
reduction),
which
is
applied
on
features
sho
wn
in
Figure
6.
Here
the
standard
de
viations,
kurtosis
and
adfs
of
open,
close,
high
and
v
olume
properties
into
single
columns
respecti
v
ely
as
std,
kurt
and
adf
columns.
PCA
reduces
the
comple
xity
by
e
xtracting
necessary
features
and
classification
process.
K-means
does
clustering
t
h
e
gi
v
en
samples
into
tw
o
clusters.
The
last
column
of
the
Figure
7
i
ndicates
cluster
1
by
0
and
cluster
2
by
1.
Figure
8
sho
ws
the
histogram
on
the
Inde
x
column
of
the
Figure
7
by
considering
only
cluster
2
samples.
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
17,
No.
2,
September
2020
:
1602
–
1609
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
1607
Figure
7.
PCA
and
K-means
clustering
results
Figure
8.
Histogram
on
sector
indices
considering
only
cluster
2
samples
The
histogram
is
clearly
declaring
that
the
second
sector
that
is
banking
sector
stocks
were
in
trend
during
the
gi
v
en
period
of
time
in
the
mark
et.
The
classification
models
certainly
impro
v
es
the
ef
ficienc
y
of
the
process
which
i
n
v
olv
es
lar
ge
a
mount
of
data.
The
e
xtracted
features
lik
e
Standard
De
viation,
K
urtosi
s
and
Dick
e
y
Fuller
T
est
ha
v
e
yielded
the
result
which
is
acceptable
as
per
the
statistics.
A
computing
model
for
tr
end
analysis
in
stoc
k
data
str
eam
classification
(Mr
.
Razak)
Evaluation Warning : The document was created with Spire.PDF for Python.
1608
r
ISSN:
2502-4752
5.
CONCLUSION
The
stock
m
ark
et
has
tremendous
opportunities
for
b
usinessman,
manuf
acturer
,
in
v
estor
and
e
v
en
for
data
analyst
to
study
the
beha
viour
of
en
vironment
and
society
.
In
this
re
g
ard,
this
paper
has
tried
to
analyze
the
stock
data
stream
to
estimate
trend
sector
inde
x
in
the
mark
et
based
on
feature
e
xtraction
and
unsupervised
clustering
(K-means)
technique.
It
has
been
implem
ented
and
demonstrated
the
results
by
fetching
stock
data
stream
from
the
serv
er
through
nsep
y
package
of
p
ython.
The
proposed
w
ork
has
not
been
considered
an
y
performance
analysis
of
the
model
and
the
same
can
be
enhanced
as
a
ne
w
proposal
t
hroug
h
big
data
analytics.
A
CKNO
WLEDGMENT
Authors
ackno
wledge
and
thank
to
Management,
Director
and
Principal
of
Bapuji
Institute
of
Engi-
neering
and
T
echnology
,
Da
v
angere
for
pro
viding
an
opportunit
y
and
platform
to
conduct
an
e
xperiment
and
produce
meaningful
results.
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