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s
e
s
o
n
teac
h
i
n
g
A
NN
to
p
er
f
o
r
m
tas
k
s
w
it
h
o
u
t
th
e
n
ee
d
f
o
r
d
ir
ec
t
p
r
o
g
r
am
m
i
n
g
.
I
t
d
er
iv
es
it
s
i
n
s
p
ir
atio
n
f
r
o
m
th
e
co
n
f
i
g
u
r
atio
n
a
n
d
o
p
er
atio
n
o
f
t
h
e
h
u
m
a
n
b
r
ain
,
p
ar
ticu
lar
l
y
n
e
u
r
al
n
et
w
o
r
k
s
,
w
h
ic
h
co
n
s
is
t
o
f
i
n
ter
co
n
n
ec
t
ed
u
n
it
s
(
n
e
u
r
o
n
s
)
r
esp
o
n
s
ib
le
f
o
r
p
r
o
ce
s
s
in
g
a
n
d
tr
an
s
m
itti
n
g
in
f
o
r
m
a
tio
n
.
Sev
er
al
r
esear
c
h
er
s
h
av
e
u
t
ili
ze
d
v
ar
io
u
s
ap
p
r
o
ac
h
es
f
o
r
f
o
r
ec
asti
n
g
ti
m
e
s
er
ies
d
ata.
I
n
r
esear
ch
b
y
R
o
o
n
d
i
w
ala
et
a
l.
[
3
]
,
an
lo
n
g
s
h
o
r
t
ter
m
m
e
m
o
r
y
(
L
ST
M)
m
o
d
el
w
a
s
d
ev
elo
p
ed
to
f
o
r
ec
ast
s
to
ck
p
r
ices.
J
o
s
e
et
a
l
.
[
4
]
p
r
esen
ted
a
n
o
v
el
ap
p
r
o
ac
h
to
ac
cu
r
ately
p
r
ed
ict
o
p
en
in
g
s
to
ck
p
r
ices
b
y
f
u
s
i
n
g
e
n
s
e
m
b
le
lear
n
in
g
m
e
th
o
d
s
w
it
h
tech
n
i
ca
l
in
d
icato
r
s
.
Saad
et
a
l.
[
5
]
u
tili
ze
d
m
ac
h
i
n
e
lear
n
i
n
g
tec
h
n
iq
u
es
to
co
n
s
tr
u
c
t
m
o
d
el
s
to
p
r
ed
ict
B
itco
in
p
r
ic
es.
T
h
ey
e
x
p
lo
r
ed
a
m
u
lti
v
ar
i
ate
r
eg
r
ess
io
n
ap
p
r
o
ac
h
,
r
el
y
i
n
g
o
n
h
i
g
h
l
y
l
in
k
ed
ch
ar
ac
ter
is
tic
s
,
an
d
a
d
ee
p
lear
n
in
g
m
ec
h
an
i
s
m
i
n
co
r
p
o
r
ates
th
e
co
n
j
u
g
ate
g
r
ad
ie
n
t
m
et
h
o
d
alo
n
g
s
id
e
li
n
ea
r
s
ea
r
ch
to
f
o
r
ec
ast
B
itco
in
’
s
p
r
ice.
Ma
h
j
o
u
b
y
et
a
l.
[
6
]
lo
o
k
ed
at
d
if
f
er
en
t
m
ac
h
i
n
e
lear
n
i
n
g
ap
p
r
o
ac
h
es
a
n
d
s
u
g
g
e
s
ted
an
e
n
s
e
m
b
le
ap
p
r
o
ac
h
th
a
t
co
m
b
in
ed
t
h
r
ee
m
o
d
el
s
:
Gau
s
s
ia
n
n
aïv
e
B
a
y
es,
lo
g
i
s
tic
r
eg
r
es
s
io
n
,
a
n
d
ex
tr
e
m
e
g
r
ad
ien
t
b
o
o
s
tin
g
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
s
o
u
g
h
t
t
o
p
r
ed
ict
th
e
b
est
ti
m
e
s
to
b
u
y
an
d
s
el
l
US
d
o
llar
s
to
th
e
J
ap
an
ese
y
e
n
to
in
cr
ea
s
e
ea
r
n
in
g
s
.
P
r
ed
ictio
n
s
o
f
cu
r
r
en
c
y
tr
en
d
s
u
tili
zi
n
g
t
h
e
L
ST
M
an
d
g
ated
r
ec
u
r
r
en
t
u
n
i
ts
(
GR
U)
al
g
o
r
ith
m
s
w
er
e
ev
a
lu
a
ted
in
[
7
]
u
s
i
n
g
v
ar
io
u
s
d
ataset
p
ar
titi
o
n
s
.
T
h
e
b
est
ac
cu
r
ate
f
i
n
d
in
g
s
,
w
it
h
0
.
0
5
4
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
0
.
0
3
7
m
ea
n
a
b
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
P
E
)
,
an
d
0
.
9
7
R
-
s
q
u
ar
ed
(
R
2
)
,
w
er
e
o
b
tain
ed
b
y
d
iv
id
in
g
a
d
atase
t
o
f
4
9
7
9
r
o
w
s
i
n
to
th
r
ee
p
ar
ts
:
8
0
%
f
o
r
t
r
ain
in
g
,
1
0
%
f
o
r
v
alid
atio
n
,
a
n
d
1
0
%
f
o
r
test
in
g
.
Ma
ch
i
n
e
lear
n
i
n
g
w
a
s
e
m
p
lo
y
ed
in
[
8
]
to
p
r
ed
ict
th
e
p
r
ice
o
f
B
itco
in
.
T
o
d
ay
,
ev
er
y
o
n
e
d
is
cu
s
s
es
wh
en
to
b
u
y
o
r
s
ell
th
e
cr
y
p
to
cu
r
r
en
c
y
b
itco
in
o
r
E
UR
/US
D.
Sev
er
al
r
esear
ch
er
s
h
av
e
d
e
v
elo
p
ed
ar
ticles
an
d
r
esear
c
h
to
ad
d
r
es
s
th
is
i
s
s
u
e.
I
n
th
i
s
co
n
te
x
t,
w
e
u
til
ized
d
iv
er
s
e
d
ee
p
-
lear
n
in
g
m
o
d
el
s
an
d
in
tr
o
d
u
ce
d
a
h
y
b
r
id
ap
p
r
o
ac
h
to
p
r
ed
ict
th
e
f
u
tu
r
e
v
alu
e
s
o
f
E
UR
/U
SD
an
d
B
itco
in
.
Ou
r
p
u
r
p
o
s
e
is
to
f
o
r
ec
ast
th
e
f
o
llo
w
i
n
g
d
a
y
.
T
h
is
f
o
r
ec
asti
n
g
o
n
f
u
n
d
a
m
e
n
tal
c
h
ar
ac
ter
is
tic
s
:
o
p
en
,
h
ig
h
,
lo
w
,
clo
s
e,
an
d
t
w
e
n
t
y
-
o
n
e
tech
n
ical
i
n
d
icato
r
s
.
T
h
e
p
ap
e
r
’
s
s
tr
u
ct
u
r
e
is
as
f
o
llo
w
s
:
th
e
s
ec
o
n
d
s
ec
tio
n
p
r
o
v
id
es
an
o
v
er
v
ie
w
o
f
t
h
e
l
iter
atu
r
e
an
d
clar
if
ies
r
elev
a
n
t
s
tu
d
ie
s
.
I
n
t
h
e
th
ir
d
s
ec
t
io
n
,
w
e
w
i
ll
i
n
tr
o
d
u
ce
t
h
e
r
ec
u
r
r
en
t
n
e
u
r
al
n
et
w
o
r
k
s
(
R
NN)
u
tili
ze
d
in
o
u
r
w
o
r
k
.
T
h
e
f
o
u
r
th
s
ec
tio
n
w
ill
d
etail
th
e
m
et
h
o
d
s
u
s
ed
.
T
h
e
f
if
th
s
ec
tio
n
w
il
l
elab
o
r
ate
o
n
th
e
p
er
f
o
r
m
a
n
ce
m
etr
ic
s
r
elev
a
n
t
to
th
is
d
o
m
ai
n
.
T
h
e
s
i
x
th
s
ec
ti
o
n
w
ill
p
r
ese
n
t
a
n
d
an
a
l
y
ze
th
e
r
esu
lt
s
,
w
h
ile
t
h
e
f
i
n
al
s
ec
tio
n
w
ill t
h
e
f
i
n
al
r
e
m
ar
k
s
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
A
r
ev
ie
w
o
f
t
h
e
p
r
ev
io
u
s
liter
atu
r
e
is
co
v
er
ed
in
th
is
s
ec
tio
n
.
C
h
e
n
et
a
l.
[
9
]
f
o
r
ec
asted
t
h
e
clo
s
in
g
p
r
ices
o
f
s
ev
er
al
b
u
s
in
e
s
s
e
s
u
s
i
n
g
a
co
m
b
in
atio
n
o
f
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
et
w
o
r
k
(
C
NN)
,
b
id
ir
ec
tio
n
al
L
ST
Ms
(
B
iLST
M)
,
an
d
an
at
ten
tio
n
-
b
ased
m
o
d
el.
T
h
e
y
u
s
ed
C
NN
to
d
er
iv
e
c
h
ar
ac
ter
is
tics
f
r
o
m
t
h
e
i
n
p
u
t
d
ata
an
d
en
h
an
ce
d
t
h
e
n
et
w
o
r
k
’
s
p
r
ed
ictiv
e
ca
p
ab
ilit
ie
s
b
y
i
n
te
g
r
atin
g
a
n
e
f
f
ic
ien
t
ch
a
n
n
el
a
t
ten
tio
n
m
ec
h
a
n
i
s
m
w
it
h
B
iL
ST
M
f
o
r
s
to
ck
p
r
ice
f
o
r
ec
asti
n
g
.
Sa
y
a
v
o
n
g
et
a
l
.
[
1
0
]
u
tili
ze
d
a
C
NN
to
p
r
ed
ict
th
e
clo
s
in
g
p
r
ice
an
d
tr
en
d
o
f
th
r
ee
f
ir
m
s
lis
ted
o
n
th
e
T
h
ai
s
to
ck
ex
ch
a
n
g
e.
T
h
e
y
e
m
p
lo
y
ed
a
s
lid
in
g
w
i
n
d
o
w
ap
p
r
o
ac
h
w
it
h
a
w
i
n
d
o
w
s
ize
m
atc
h
in
g
th
e
i
n
p
u
t
s
ize.
I
n
ea
ch
iter
atio
n
o
f
t
h
e
s
lid
i
n
g
w
i
n
d
o
w
,
t
h
er
e
w
a
s
n
o
o
v
er
la
p
b
etw
ee
n
co
n
s
ec
u
ti
v
e
w
i
n
d
o
w
s
a
s
th
e
y
m
o
v
ed
to
th
e
r
ig
h
t.
T
h
e
o
u
tco
m
es
r
ev
e
aled
th
at
th
e
C
NN
m
o
d
el
ex
h
ib
ited
ex
ce
lle
n
t
p
er
f
o
r
m
a
n
ce
in
f
o
r
ec
asti
n
g
t
h
e
cu
r
r
en
t d
a
y
’
s
d
ata.
Fab
b
r
i
an
d
Mo
r
o
[
1
1
]
,
u
tili
ze
d
an
L
ST
M
m
o
d
el
f
o
r
f
o
r
ec
asti
n
g
th
e
clo
s
i
n
g
p
r
ice
o
f
th
e
Do
w
J
o
n
e
s
in
d
ex
f
o
r
th
e
n
e
x
t d
a
y
.
T
h
e
y
c
o
n
d
u
cted
a
co
m
p
ar
is
o
n
b
et
w
e
en
th
e
r
es
u
lt
s
o
b
tain
ed
f
r
o
m
th
e
L
ST
M
m
o
d
el
an
d
th
o
s
e
f
r
o
m
a
f
ee
d
-
f
o
r
w
ar
d
n
eu
r
al
n
et
w
o
r
k
(
FF
N
N)
.
T
h
e
r
esu
lt
s
o
b
tain
ed
in
d
icate
d
th
a
t
th
e
L
ST
M
m
o
d
el
o
u
tp
er
f
o
r
m
ed
th
e
f
ee
d
-
f
o
r
w
ar
d
n
eu
r
al
n
et
w
o
r
k
in
ter
m
s
o
f
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
Oj
o
et
a
l.
[
1
2
]
u
tili
ze
d
a
d
ee
p
lear
n
in
g
tech
n
iq
u
e
k
n
o
w
n
a
s
s
tack
ed
L
ST
M
to
f
o
r
ec
ast
th
e
clo
s
in
g
p
r
ic
e
o
f
th
e
N
ASD
A
Q
co
m
p
o
s
ite
o
n
th
e
Am
er
ica
n
s
to
ck
e
x
ch
a
n
g
e
.
T
h
e
o
u
tco
m
es
o
f
t
h
eir
s
t
u
d
y
d
em
o
n
s
tr
ated
a
n
e
n
h
an
ce
m
e
n
t
in
p
r
ed
icti
v
e
ac
c
u
r
ac
y
w
h
e
n
f
o
r
ec
asti
n
g
t
h
e
s
to
c
k
p
r
ice.
L
i
n
et
a
l.
[
1
3
]
u
tili
ze
d
a
L
ST
M
m
o
d
el
to
f
o
r
ec
ast
th
e
clo
s
in
g
p
r
ice
u
s
i
n
g
h
i
s
to
r
ical
s
to
ck
d
ata
T
aiw
a
n
s
to
ck
e
x
ch
an
g
e
(
T
W
SE)
.
T
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
S
imp
le
R
N
N
-
LS
TM
h
yb
r
id
d
ee
p
lea
r
n
in
g
mo
d
el
fo
r
B
itco
in
a
n
d
E
UR
_
US
D
…
(
Mo
h
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med
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L Ma
h
jo
u
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177
p
r
im
ar
y
o
b
j
ec
tiv
e
w
a
s
to
s
u
b
s
titu
te
t
h
e
R
NN
m
o
d
el
w
i
th
t
h
e
L
ST
M
m
o
d
el
b
ec
au
s
e
o
f
its
ca
p
ab
ilit
y
f
o
r
lo
n
g
-
ter
m
m
e
m
o
r
y
r
ete
n
tio
n
,
as t
h
e
R
NN
s
tr
u
g
g
led
to
m
ai
n
tai
n
d
a
ta
o
v
er
ex
ten
d
ed
p
er
io
d
s
.
Ma
k
ala
an
d
L
i
[
1
4
]
in
v
e
s
ti
g
ated
g
o
ld
co
m
m
o
d
it
y
f
o
r
ec
asti
n
g
u
s
i
n
g
au
to
r
eg
r
es
s
i
v
e
i
n
teg
r
ated
m
o
v
i
n
g
av
e
r
ag
e
(
A
R
I
M
A
)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
e
(
SV
M)
,
an
d
th
r
ee
ty
p
es
o
f
k
er
n
el
s
w
er
e
e
m
p
lo
y
ed
in
th
is
p
ap
er
.
B
itco
in
is
a
co
m
m
o
n
l
y
u
tili
ze
d
cr
y
p
to
cu
r
r
en
c
y
i
n
th
e
p
r
ese
n
t
d
a
y
,
an
d
as
a
r
es
u
lt,
th
er
e
ar
e
o
n
l
y
a
li
m
ited
n
u
m
b
er
o
f
p
r
ed
ictio
n
m
o
d
el
s
ac
ce
s
s
ib
le.
Mc
Nall
y
et
a
l.
[
1
5
]
u
s
e
R
NN
co
u
p
led
w
i
t
h
L
ST
M
to
ac
h
iev
e
5
2
%
class
i
f
icatio
n
ac
cu
r
ac
y
an
d
an
R
MSE
o
f
8
%.
T
h
e
au
th
o
r
s
a
s
s
er
t
th
at
th
e
u
t
iliz
atio
n
o
f
R
NN
in
co
n
j
u
n
ctio
n
w
i
th
L
ST
M
y
ield
s
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
co
m
p
a
r
ed
to
co
n
v
en
tio
n
al
R
NN
a
n
d
AR
I
M
A
m
o
d
els.
B
itco
in
p
r
ice
p
r
ed
ictio
n
is
co
n
d
u
cted
th
r
o
u
g
h
e
m
p
ir
ical
i
n
v
e
s
ti
g
atio
n
[
1
6
]
.
T
h
e
B
ay
es
ian
n
e
u
r
a
l
n
et
w
o
r
k
i
s
co
m
p
ar
ed
to
p
r
o
v
en
n
o
n
-
l
in
ea
r
a
n
d
lin
ea
r
b
en
c
h
m
ar
k
m
eth
o
d
s
i
n
t
h
is
s
tu
d
y
,
w
h
ic
h
g
i
v
es
u
s
e
f
u
l
in
s
i
g
h
ts
.
Su
n
et
a
l
[
1
7
]
d
is
ce
r
n
ed
d
ail
y
tr
e
n
d
s
i
n
t
h
e
B
itc
o
in
m
ar
k
et
w
h
ile
co
n
s
id
er
i
n
g
th
e
s
e
m
a
n
tic
s
a
n
d
in
s
i
g
h
ts
r
elate
d
to
f
ac
to
r
s
in
f
l
u
en
ci
n
g
B
itco
in
’
s
p
r
ice.
T
h
e
y
also
f
o
r
ec
asted
ch
an
g
e
s
in
B
itco
in
’
s
d
ail
y
p
r
ice,
e
m
p
lo
y
i
n
g
b
o
th
R
a
n
d
o
m
Fo
r
est an
d
B
a
y
esia
n
r
eg
r
es
s
io
n
m
e
th
o
d
s
as p
ar
t o
f
th
eir
r
esear
ch
.
3.
NE
URA
L
N
E
T
WO
RK
S
W
e
lo
o
k
ed
at
th
e
v
ar
io
u
s
d
ee
p
lear
n
in
g
m
o
d
els
u
s
ed
in
t
h
i
s
w
o
r
k
i
n
th
is
p
ar
t.
Dee
p
lear
n
in
g
n
e
u
r
al
n
et
w
o
r
k
s
(
DL
N
N)
o
r
g
an
ize
e
n
o
r
m
o
u
s
a
m
o
u
n
ts
o
f
d
ata
b
y
a
r
r
an
g
i
n
g
co
m
p
licated
d
ata
s
tr
u
ctu
r
es
t
h
at
an
ANN
ca
n
n
o
t
[
1
8
]
.
A
d
ee
p
n
eu
r
al
n
e
t
w
o
r
k
i
s
a
p
ar
allel
p
r
o
ce
s
s
in
g
ar
ch
itectu
r
e
m
ad
e
u
p
o
f
in
ter
co
n
n
ec
ted
n
e
u
r
o
n
s
o
r
g
an
ized
in
a
la
y
er
n
et
w
o
r
k
.
T
h
e
p
r
im
ar
y
s
tr
u
ct
u
r
e
o
f
a
d
ee
p
n
eu
r
al
n
et
w
o
r
k
is
co
m
p
o
s
ed
o
f
th
r
ee
i
m
p
o
r
tan
t
co
m
p
o
n
e
n
t
s
: i
n
p
u
t,
o
u
tp
u
t,
an
d
o
n
e
o
r
m
o
r
e
h
id
d
en
la
y
er
s
o
f
h
id
d
en
u
n
it
s
[
1
9
]
.
3
.
1
.
Rec
urre
nt
neura
l net
wo
rks
Hu
m
an
co
m
p
r
eh
e
n
s
io
n
o
f
ea
ch
s
ce
n
e
i
n
a
m
o
v
ie
r
elies
o
n
g
r
asp
in
g
th
e
co
n
te
x
t
esta
b
lis
h
ed
b
y
p
r
ec
ed
in
g
s
ce
n
e
s
.
C
o
n
v
en
tio
n
al
n
eu
r
al
n
et
w
o
r
k
s
ar
e
in
ca
p
ab
le
o
f
ac
co
m
p
lis
h
i
n
g
th
i
s
,
p
o
s
in
g
a
s
ig
n
i
f
ican
t
ch
alle
n
g
e.
R
NN
s
tac
k
le
t
h
is
p
r
o
b
lem
b
y
i
n
co
r
p
o
r
atin
g
lo
o
p
s
,
en
ab
li
n
g
th
e
m
to
r
etain
a
n
d
u
tili
ze
in
f
o
r
m
a
tio
n
o
v
er
ti
m
e.
R
NNs
f
i
n
d
u
tili
t
y
i
n
v
ar
io
u
s
ap
p
licatio
n
s
in
v
o
lv
i
n
g
s
eq
u
en
t
ial
d
ata,
in
clu
d
i
n
g
t
ask
s
lik
e
g
e
n
er
ati
n
g
ca
p
tio
n
s
f
o
r
i
m
a
g
es
o
r
v
id
eo
s
,
n
atu
r
al
la
n
g
u
a
g
e
p
r
o
ce
s
s
i
n
g
s
u
c
h
as
p
r
ed
ictin
g
w
o
r
d
s
,
tr
a
n
s
lat
in
g
la
n
g
u
a
g
es,
an
d
r
ec
o
g
n
izi
n
g
s
p
ee
ch
.
Fig
u
r
e
1
illu
s
tr
ates
a
n
e
u
r
al
n
et
w
o
r
k
e
le
m
e
n
t
lab
eled
as
“
A.
”
T
h
is
ele
m
e
n
t
ac
ce
p
ts
a
n
i
n
p
u
t
d
en
o
te
d
as
“
”
an
d
p
r
o
d
u
ce
s
an
o
u
tp
u
t
v
alu
e
r
e
f
er
r
ed
to
as
“
ℎ
.
”
T
h
e
in
cl
u
s
io
n
o
f
a
lo
o
p
en
ab
les
th
e
ex
ch
a
n
g
e
o
f
in
f
o
r
m
atio
n
b
et
w
ee
n
v
ar
io
u
s
s
tep
s
w
i
th
i
n
t
h
e
n
e
t
w
o
r
k
.
Yo
u
ca
n
en
v
is
io
n
a
r
ec
u
r
r
en
t
n
e
u
r
al
n
et
w
o
r
k
as
h
a
v
in
g
m
u
ltip
le
i
n
s
ta
n
ce
s
o
f
“
A
”
w
i
t
h
in
th
e
n
et
w
o
r
k
,
w
h
er
e
ea
c
h
i
n
s
ta
n
ce
s
eq
u
en
tiall
y
s
e
n
d
s
i
n
f
o
r
m
at
io
n
to
t
h
e
n
e
x
t
o
n
e,
as d
ep
icted
in
Fig
u
r
e
1
.
Fig
u
r
e
1
.
An
u
n
r
o
lled
r
ep
r
esen
tatio
n
o
f
a
r
ec
u
r
r
en
t
n
eu
r
al
n
et
w
o
r
k
I
n
s
itu
atio
n
s
w
h
er
e
th
e
d
is
ta
n
ce
b
etw
ee
n
p
er
tin
en
t
in
f
o
r
m
at
io
n
an
d
th
e
p
o
in
t
w
h
er
e
it
is
r
eq
u
ir
ed
is
m
i
n
i
m
al,
R
NN
s
ca
n
ac
q
u
ir
e
th
e
ab
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y
to
m
ak
e
u
s
e
o
f
p
ast
in
f
o
r
m
atio
n
e
f
f
ec
ti
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y
.
Ho
wev
er
,
s
itu
a
tio
n
s
ca
n
o
cc
u
r
w
h
er
e
a
b
r
o
ad
er
co
n
tex
t
b
ec
o
m
es
es
s
e
n
tial,
an
d
i
n
th
ese
ca
s
e
s
,
th
e
d
is
ta
n
ce
b
et
w
ee
n
t
h
e
r
eq
u
ir
ed
in
f
o
r
m
atio
n
a
n
d
th
e
m
o
m
en
t
it
is
n
e
ed
ed
ca
n
s
ig
n
i
f
ican
tl
y
i
n
cr
ea
s
e
.
I
n
p
r
ac
tical
ter
m
s
,
R
NNs
ten
d
to
s
tr
u
g
g
l
e
w
it
h
t
h
is
la
tter
s
ce
n
ar
io
,
as
n
o
ted
in
r
ef
er
en
ce
s
[2
0]
.
3
.
2
.
L
o
ng
s
ho
rt
t
er
m
m
e
m
o
ry
L
ST
M
n
et
w
o
r
k
s
tac
k
le
t
h
e
i
s
s
u
es
o
f
d
i
m
in
i
s
h
in
g
a
n
d
s
k
y
r
o
c
k
eti
n
g
g
r
ad
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t
p
r
o
b
lem
s
t
h
at
f
r
eq
u
en
tl
y
o
cc
u
r
in
ex
te
n
d
ed
r
ec
u
r
r
en
t
n
eu
r
al
n
et
w
o
r
k
s
(
E
R
NN
s
)
an
d
m
o
r
e
g
en
er
all
y
,
i
n
tr
ad
itio
n
al
R
NNs
[
2
1
]
.
L
ST
M
n
et
w
o
r
k
s
r
etain
t
h
e
id
en
tical
o
v
er
all
ar
ch
itect
u
r
al
la
y
o
u
t
as
E
R
NNs
b
u
t
d
is
tin
g
u
is
h
t
h
e
m
s
elv
e
s
b
y
h
a
v
i
n
g
a
d
if
f
er
e
n
t
co
n
f
ig
u
r
atio
n
f
o
r
th
e
in
ter
n
al
m
o
d
u
le
o
r
ce
ll
.
L
STM
is
a
ty
p
e
o
f
R
NN
t
h
at
h
a
n
d
les
an
d
p
r
ed
icts
d
ata
w
it
h
r
ec
u
r
r
en
t
ti
m
e
s
er
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s
i
n
an
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f
ec
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m
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n
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er
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T
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m
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ies
f
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m
a
n
y
d
o
m
ai
n
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it
f
i
n
d
s
u
s
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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6
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3
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6930
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Feb
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20
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1
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178
3
.
3
.
G
a
t
ed
re
curr
ent
un
it
s
I
n
itiall
y
p
r
esen
ted
in
C
h
o
et
a
l
.
[
2
2
]
,
GR
Us
r
ep
r
es
en
t
a
s
i
m
p
li
f
ied
v
er
s
io
n
o
f
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ST
M
an
d
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n
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e
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al
l
w
it
h
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n
th
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teg
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N
N
s
.
GR
Us
s
et
t
h
e
m
s
el
v
es
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ar
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f
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o
m
L
ST
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y
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atin
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t
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n
ct
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s
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h
ich
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e
r
eg
u
lated
b
y
t
h
e
f
o
r
g
et
g
ate
a
n
d
t
h
e
i
n
p
u
t
g
ate,
i
n
to
a
s
i
n
g
l
e
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ate.
T
h
is
t
y
p
e
o
f
ce
ll
u
lti
m
at
el
y
i
n
co
r
p
o
r
ates
o
n
l
y
t
w
o
g
at
es,
lead
in
g
to
a
m
o
r
e
co
n
cise
ar
ch
itect
u
r
e
w
h
e
n
co
n
tr
asted
w
it
h
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ST
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w
h
ic
h
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n
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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23
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Feb
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20
25
:
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75
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1
84
184
K
h
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strial
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ls).
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c
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n
b
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c
o
n
ta
c
ted
a
t
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m
a
il
:
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h
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m
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la
m
rin
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sm
b
a
.
a
c
.
m
a
.
M
o
h
a
m
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l
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ar
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c
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rre
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tl
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a
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f
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r
w
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h
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o
h
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m
e
d
Be
n
A
b
d
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ll
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h
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rsit
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,
F
e
z
,
M
o
ro
c
c
o
.
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te
a
c
h
e
s
c
o
m
p
u
ter
sc
ien
c
e
.
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re
s
e
a
rc
h
in
tere
sts
in
c
lu
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d
a
ta
sc
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c
e
,
b
ig
d
a
ta,
a
n
d
a
rti
f
icia
l
in
telli
g
e
n
c
e
.
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h
a
d
p
u
b
li
s
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e
d
m
a
n
y
r
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se
a
r
c
h
e
rs.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
h
a
m
e
d
.
e
l
f
a
r@u
sm
b
a
.
a
c
.
m
a
.
Evaluation Warning : The document was created with Spire.PDF for Python.