I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
pu
t
er
E
ng
ineering
(
I
J
E
CE
)
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
,
p
p
.
1339
~
1
3
4
9
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijece.
v
1
6
i
3
.
pp
1
3
3
9
-
1
3
4
9
1339
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m
Ev
a
lua
tion o
f
ma
chine learni
ng
appro
a
ch in
mo
delli
ng
and
foreca
sting
real g
ro
ss
dom
estic pro
duct
g
ro
wt
h:
a
co
mpa
ra
tive
study
M
o
iz
Q
uresh
i
1
,
M
u
ha
m
m
a
d
I
s
m
a
il
2
,
Na
wa
z
Ahm
a
d
3,
4
,
I
bra
r
H
us
s
a
in
5
,
Abba
s
A
li G
ho
t
o
6
,
J
o
lita
Vv
einha
rdt
7
1
G
o
v
e
r
n
m
e
n
t
D
e
g
r
e
e
B
o
y
s
C
o
l
l
e
g
e
T
a
n
d
o
J
a
m,
H
y
d
e
r
a
b
a
d
S
i
n
d
h
,
Ta
n
d
o
Jā
n
M
o
h
a
mm
a
d
,
P
a
k
i
st
a
n
2
D
e
p
a
r
t
me
n
t
o
f
S
t
a
t
i
s
t
i
c
s,
Q
u
a
i
d
-
i
-
A
z
a
m U
n
i
v
e
r
si
t
y
,
I
sl
a
ma
b
a
d
,
P
a
k
i
s
t
a
n
3
D
e
p
a
r
t
me
n
t
o
f
B
u
si
n
e
ss
A
d
m
i
n
i
s
t
r
a
t
i
o
n
,
C
o
n
v
e
n
e
r
o
f
t
h
e
R
e
se
a
r
c
h
S
o
c
i
e
t
y
,
S
h
a
h
e
e
d
B
e
n
a
z
i
r
B
h
u
t
t
o
U
n
i
v
e
r
s
i
t
y
,
S
i
n
d
h
,
P
a
k
i
st
a
n
4
G
o
v
e
r
n
a
n
c
e
,
C
o
m
p
e
t
i
t
i
v
e
n
e
ss
a
n
d
P
u
b
l
i
c
P
o
l
i
c
i
e
s
(
G
O
V
C
O
P
P
)
,
U
n
i
v
e
r
si
t
y
o
f
A
v
e
i
r
o
,
A
v
e
i
r
o
,
P
o
r
t
u
g
a
l
5
D
e
p
a
r
t
me
n
t
o
f
S
t
a
t
i
s
t
i
c
s,
A
b
d
u
l
W
a
l
i
K
h
a
n
U
n
i
v
e
r
s
i
t
y
M
a
r
d
a
n
,
M
a
r
d
a
n
,
P
a
k
i
st
a
n
6
D
e
p
a
r
t
me
n
t
o
f
B
a
si
c
S
c
i
e
n
c
e
a
n
d
R
e
l
a
t
e
d
S
t
u
d
i
e
s
,
Q
u
a
i
d
-
e
-
A
w
a
m U
n
i
v
e
r
s
i
t
y
o
f
En
g
i
n
e
e
r
i
n
g
,
S
c
i
e
n
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
,
S
i
n
d
h
,
P
a
k
i
s
t
a
n
7
V
y
t
a
u
t
a
s
K
a
v
o
l
i
s
Tr
a
n
sd
i
sc
i
p
l
i
n
a
r
y
S
o
c
i
a
l
a
n
d
H
u
m
a
n
i
t
i
e
s
R
e
se
a
r
c
h
I
n
st
i
t
u
t
e
,
V
y
t
a
u
t
a
s
M
a
g
n
u
s U
n
i
v
e
r
s
i
t
y
,
K
a
u
n
a
s,
L
i
t
h
u
a
n
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
2
3
,
2
0
2
4
R
ev
is
ed
J
an
2
4
,
2
0
2
6
Acc
ep
ted
Ma
r
1
6
,
2
0
2
6
Th
is
stu
d
y
a
ims
to
p
r
o
v
i
d
e
a
n
e
fficie
n
t
a
n
d
a
c
c
u
ra
te
m
a
c
h
in
e
-
lea
rn
in
g
a
p
p
ro
a
c
h
f
o
r
m
o
d
e
ll
in
g
a
n
d
fo
re
c
a
stin
g
th
e
re
a
l
g
r
o
ss
d
o
m
e
stic
p
ro
d
u
c
ti
o
n
(
G
DP
)
in
th
e
c
o
n
te
x
t
o
f
P
a
k
is
tan
.
Th
e
st
u
d
y
f
o
re
c
a
sts
P
a
k
istan
'
s
GD
P
g
ro
wt
h
ra
te
u
sin
g
d
iffere
n
t
f
o
re
c
a
stin
g
m
o
d
e
ls,
su
c
h
a
s
n
a
ï
v
e
,
se
a
so
n
a
l
n
a
ïv
e
(S
Na
iv
e
)
,
sm
o
o
t
h
in
g
,
a
n
d
k
-
n
e
a
re
st
n
e
ig
h
b
o
rs
(k
-
NN
).
M
a
c
h
i
n
e
lea
rn
in
g
a
lg
o
rit
h
m
s
p
ro
v
id
e
a
d
d
it
i
o
n
a
l
a
d
v
ice
fo
r
d
a
ta
-
d
riv
e
n
d
e
c
isio
n
-
m
a
k
in
g
.
Ac
c
o
rd
in
g
t
o
th
e
fi
n
d
i
n
g
s,
t
h
e
k
-
NN
-
b
a
se
d
fo
re
c
a
stin
g
g
iv
e
s
m
in
i
m
u
m
m
e
a
n
a
b
so
lu
te
p
e
rc
e
n
tag
e
e
rro
r
(M
A
P
E),
ro
o
t
m
e
a
n
sq
u
a
re
e
rro
r
(
R
M
S
E
)
,
a
n
d
m
e
a
n
a
b
so
lu
te
e
rro
r
(M
AE)
c
o
m
p
a
re
d
t
o
th
e
o
t
h
e
r
t
h
re
e
m
o
d
e
ls.
Eco
n
o
m
ic
p
o
li
c
y
m
a
k
e
rs
c
a
n
u
se
a
c
c
u
ra
te
m
o
d
e
ls
to
m
e
a
su
re
sig
n
ifi
c
a
n
t
e
c
o
n
o
m
ic
a
c
ti
v
it
y
a
n
d
fo
rm
u
late
p
lan
s.
Th
e
re
su
lt
s
i
n
d
ica
te
t
h
a
t
t
h
e
m
o
d
e
l
p
r
o
d
u
c
e
d
a
c
c
u
ra
te p
ro
jec
ti
o
n
s
o
f
f
u
tu
re
G
DP
lev
e
ls f
o
r
P
a
k
istan
.
K
ey
w
o
r
d
s
:
E
co
n
o
m
ic
p
lan
n
in
g
F
o
r
ec
asti
n
g
GDP
g
r
o
wth
r
ate
k
-
NN
Ma
ch
in
e
lear
n
in
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
J
o
lita V
v
ein
h
ar
d
t
Vy
tau
tas Ka
v
o
lis
T
r
an
s
d
is
cip
lin
ar
y
So
cial
an
d
Hu
m
an
ities
R
esear
ch
I
n
s
titu
te
,
Vy
tau
tas M
ag
n
u
s
Un
iv
er
s
ity
K.
Do
n
elaič
io
g
.
5
8
,
Kau
n
as,
4
4
2
4
8
Kau
n
o
m
.
s
av
.
,
L
itu
an
i
a
E
m
ail:
jo
lita.v
v
ein
h
ar
d
t@
v
d
u
.
lt
1.
I
NT
RO
D
UCT
I
O
N
Data
f
r
o
m
th
e
W
o
r
ld
B
an
k
(
W
B
)
s
u
g
g
est
th
at
Pak
is
tan
’
s
g
r
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
(
GDP
)
s
to
o
d
at
US$
3
4
6
.
3
4
b
illi
o
n
at
t
h
e
en
d
o
f
2
0
2
1
.
Pak
is
tan
is
r
a
n
k
ed
a
m
o
n
g
l
o
wer
-
m
id
d
le
-
in
co
m
e
c
o
u
n
tr
ies
with
a
GDP
p
er
ca
p
ita
o
f
US$
1
,
5
3
7
.
9
.
T
h
e
W
B
est
im
ated
Pak
is
tan
’
s
g
r
o
wth
r
ate
at
6
.
0
% in
2
0
2
1
[
1
]
.
On
e
m
eth
o
d
f
o
r
ev
al
u
atin
g
an
ec
o
n
o
m
y
'
s
o
u
tp
u
t
an
d
n
atio
n
al
in
co
m
e
is
to
lo
o
k
at
th
e
G
DP
o
r
g
r
o
s
s
d
o
m
esti
c
in
co
m
e
(
GDI
)
.
T
h
e
to
tal
m
ar
k
et
v
alu
e
o
f
all
th
e
g
o
o
d
s
an
d
s
er
v
ices
p
r
o
d
u
ce
d
i
n
a
n
atio
n
d
u
r
in
g
a
s
p
ec
if
ic
p
er
io
d
is
th
e
GDP
(
u
s
u
ally
a
ca
len
d
a
r
y
ea
r
)
.
Fu
r
th
er
m
o
r
e,
it
is
r
eg
ar
d
ed
as
th
e
t
o
tal
v
alu
e
a
d
d
ed
at
ea
ch
lev
el
o
f
p
r
o
d
u
ctio
n
(
t
h
e
in
ter
m
ed
iate
s
tag
es)
o
f
all
co
m
p
leted
g
o
o
d
s
an
d
s
er
v
ices p
r
o
d
u
ce
d
with
in
a
s
tate
o
v
er
a
s
p
ec
if
ic
p
er
io
d
,
an
d
it is
ass
ig
n
ed
a
m
o
n
etar
y
v
alu
e
[
2
]
.
GDP
is
co
m
p
u
ted
as
:
=
+
+
+
(
−
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
3
9
-
1
3
4
9
1340
GDP
g
r
o
wth
r
ate
m
ea
s
u
r
es
th
e
r
elativ
e
ch
an
g
e
in
GDP
in
t
h
e
cu
r
r
e
n
t
y
ea
r
u
s
in
g
t
h
e
p
r
e
v
io
u
s
y
ea
r
’
s
GDP
as
th
e
b
ase.
T
h
e
GDP
g
r
o
wth
r
at
e
is
co
m
p
u
ted
as
:
ℎ
=
−
×
100
GDP
an
d
GDP
g
r
o
wth
r
ate
a
r
e
s
ig
n
if
ican
t
in
d
icato
r
s
o
f
an
ec
o
n
o
m
y
'
s
o
v
er
all
ec
o
n
o
m
ic
p
ictu
r
e.
Mo
n
etar
y
p
o
licy
d
ir
ec
tly
i
m
p
ac
ts
ev
er
y
ec
o
n
o
m
y
an
d
h
as lo
n
g
an
d
f
lu
ctu
atin
g
lag
s
.
Acc
u
r
ate
p
r
ed
ictio
n
s
o
f
ec
o
n
o
m
ic
ac
tiv
ity
a
r
e
ess
en
tial
f
o
r
f
o
r
m
u
latin
g
c
u
r
r
en
t
a
n
d
f
u
tu
r
e
m
ac
r
o
ec
o
n
o
m
ic
p
o
licies.
Su
s
tain
ab
le
ec
o
n
o
m
ic
g
r
o
wth
is
o
f
u
tm
o
s
t
im
p
o
r
tan
ce
f
o
r
ev
er
y
ec
o
n
o
m
y
,
esp
ec
ially
th
e
em
er
g
i
n
g
o
n
es
th
at
f
r
eq
u
en
tly
ex
p
er
ien
ce
d
if
f
icu
lties
.
T
h
u
s
,
ec
o
n
o
m
is
ts
h
av
e
co
n
ce
n
tr
ated
o
n
ex
am
in
in
g
h
o
w
GDP
m
ay
en
h
an
ce
ec
o
n
o
m
ic
g
r
o
wth
.
T
h
r
ee
m
eth
o
d
s
,
th
e
in
co
m
e
ap
p
r
o
ac
h
,
th
e
s
p
en
d
in
g
ap
p
r
o
ac
h
,
a
n
d
th
e
p
r
o
d
u
ct
ap
p
r
o
ac
h
,
ca
n
b
e
u
s
ed
to
d
escr
ib
e
GDP.
Acc
o
r
d
in
g
to
th
e
r
u
le
o
f
in
co
m
e
ap
p
r
o
ac
h
,
ea
ch
m
an
u
f
ac
tu
r
e
r
'
s
in
co
m
e
m
u
s
t
m
atch
t
h
e
v
al
u
e
o
f
t
h
eir
p
r
o
d
u
ct,
an
d
t
h
e
GDP
is
ca
lcu
lated
b
y
ad
d
i
n
g
th
e
in
co
m
es
o
f
all
p
r
o
d
u
ce
r
s
[
3
]
.
Fin
an
cial
f
o
r
ec
asti
n
g
u
n
d
o
u
b
ted
ly
in
clu
d
es
ev
alu
atin
g
t
h
e
ec
o
n
o
m
y
'
s
cu
r
r
en
t
s
tatu
s
b
ec
au
s
e
it
s
er
v
es
as
t
h
e
p
latf
o
r
m
f
o
r
a
lo
n
g
e
r
-
ter
m
s
tu
d
y
.
T
h
is
is
r
elev
a
n
t
all
th
e
m
o
r
e,
co
n
s
id
er
in
g
th
at
q
u
ar
ter
ly
GDP
g
r
o
wth
an
d
l
o
n
g
er
-
te
r
m
p
r
e
d
ictab
ilit
y
h
av
e
d
ec
r
ea
s
ed
[
4
]
.
T
h
o
u
g
h
th
er
e
is
n
o
d
o
u
b
t
th
at
f
o
r
ec
asti
n
g
s
ig
n
if
ican
tly
f
u
n
ct
io
n
s
in
m
an
y
ar
ea
s
o
f
b
u
s
in
e
s
s
,
g
o
v
er
n
m
en
t,
an
d
p
o
licy
m
a
k
in
g
,
Pil
s
tr
ö
m
an
d
Po
h
l
[
5
]
claim
s
th
at
th
e
to
p
ic
h
as
b
ee
n
o
v
e
r
lo
o
k
ed
.
Pil
s
tr
ö
m
an
d
Po
h
l
[
5
]
also
claim
s
th
at
f
ew
ec
o
n
o
m
ics
d
ep
ar
tm
en
ts
o
f
f
er
tr
ain
in
g
in
t
h
e
ar
ea
a
n
d
t
h
at
m
o
s
t
ec
o
n
o
m
etr
ic
an
d
g
r
o
wth
th
eo
r
y
tex
ts
o
n
ly
b
r
ief
ly
a
d
d
r
ess
th
e
is
s
u
e.
A
lo
t
o
f
liter
atu
r
e
h
as
s
u
g
g
ested
s
ev
er
al
m
et
h
o
d
s
f
o
r
p
r
ed
ictin
g
GDP.
T
h
e
m
ac
r
o
ec
o
n
o
m
ic
liter
atu
r
e
th
at
ex
a
m
in
es
th
is
s
u
b
ject
u
s
in
g
a
tim
e
s
er
ie
s
ap
p
r
o
ac
h
p
r
im
ar
ily
em
p
lo
y
s
v
ar
io
u
s
v
ec
to
r
au
to
r
eg
r
ess
iv
e
(
VAR)
s
p
ec
if
i
ca
tio
n
s
[
6
]
.
Fo
r
ec
asti
n
g
en
h
a
n
ce
m
en
ts
ca
n
b
e
m
a
d
e
u
s
in
g
th
e
p
r
o
p
er
B
ay
esian
s
h
r
in
k
ag
e
p
r
o
ce
d
u
r
es,
as h
ig
h
l
ig
h
ted
in
B
ab
u
r
a
[
7
]
.
Ma
n
y
s
ch
o
lar
s
b
eliev
e
t
h
at
t
h
e
y
ield
cu
r
v
e,
w
h
ich
p
r
o
v
id
es
in
s
ig
h
t
in
to
f
u
tu
r
e
ec
o
n
o
m
ic
ac
tiv
ity
,
s
h
o
u
ld
b
e
co
n
s
id
er
ed
o
n
e
o
f
th
e
p
o
ten
tial e
co
n
o
m
ic
in
d
icato
r
s
th
at
m
u
s
t b
e
em
p
lo
y
ed
b
y
GDP
f
o
r
ec
aster
s
[
8
]
.
An
o
th
er
g
r
o
u
p
o
f
s
tu
d
ies
d
e
m
o
n
s
tr
ated
th
at
p
r
o
jectio
n
s
b
ased
o
n
m
u
ltip
le
in
d
icatio
n
s
p
er
f
o
r
m
s
ig
n
if
ican
tly
b
etter
th
an
th
o
s
e
b
ased
o
n
o
n
ly
o
n
e
in
d
icato
r
[
9
]
.
T
h
e
im
p
le
m
en
tatio
n
o
f
f
o
r
ec
ast
co
m
b
in
a
tio
n
m
eth
o
d
s
is
th
e
s
ec
o
n
d
co
n
tr
ib
u
tio
n
o
f
o
u
r
in
v
esti
g
atio
n
.
Fo
r
c
o
m
b
in
i
n
g
lead
in
g
in
d
icato
r
f
o
r
ec
asts
f
o
r
I
P,
we
u
s
e
a
v
ar
iety
o
f
weig
h
tin
g
tech
n
i
q
u
es,
in
clu
d
i
n
g
s
im
p
le
av
er
a
g
in
g
s
ch
em
es
(
m
ea
n
an
d
m
ed
ian
f
o
r
e
ca
s
t)
,
tr
im
m
ed
m
ea
n
s
(
d
u
e
to
h
is
to
r
ical
o
u
t
-
of
-
s
am
p
le
p
er
f
o
r
m
a
n
ce
s
)
,
f
o
r
ec
asts
b
ased
o
n
in
-
s
am
p
le
cr
iter
ia,
wei
g
h
ts
ca
lcu
lated
b
y
r
elativ
e
m
ea
n
s
q
u
ar
e
f
o
r
e
ca
s
t e
r
r
o
r
s
,
o
r
d
in
ar
y
least sq
u
ar
e
(
OL
S)
weig
h
ts
,
an
d
s
h
r
in
k
ag
e
t
ec
h
n
iq
u
es
[
1
0
]
.
Ma
ch
in
e
lear
n
in
g
(
ML
)
h
as
m
ad
e
en
o
r
m
o
u
s
im
p
r
o
v
e
m
en
ts
in
th
e
last
f
ew
y
ea
r
s
,
esp
ec
iall
y
f
o
r
jo
b
s
in
v
o
lv
in
g
r
ec
o
g
n
itio
n
.
I
t
h
as
b
ee
n
d
em
o
n
s
tr
ated
th
at
ML
is
ex
tr
em
ely
ef
f
icien
t
at
h
an
d
lin
g
h
u
g
e
am
o
u
n
ts
o
f
d
ata
an
d
p
er
f
o
r
m
in
g
alg
o
r
ith
m
s
in
an
ad
eq
u
ate
am
o
u
n
t
o
f
tim
e,
all
wh
ile
m
ain
tain
in
g
a
b
it
in
ex
p
en
s
iv
e
co
s
ts
[
1
1
]
.
B
ey
o
n
d
s
p
ee
ch
an
d
im
a
g
e
r
ec
o
g
n
itio
n
,
it
h
as
also
d
e
m
o
n
s
tr
ated
p
o
ten
tial
in
p
r
ed
ic
tio
n
task
s
.
I
n
th
e
ML
m
o
d
el,
n
o
n
ee
d
t
o
r
eq
u
i
r
e
th
e
s
tatio
n
ar
ity
ass
u
m
p
tio
n
s
,
n
o
n
l
in
ea
r
tim
e
s
er
ies
d
ata
m
ay
b
e
p
r
ed
icted
v
e
r
y
well
u
s
in
g
ML
m
o
d
els,
s
u
ch
th
e
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
k
-
N
N)
ap
p
r
o
ac
h
.
Ho
wev
e
r
,
tr
a
d
itio
n
al
tim
e
s
er
ie
s
f
o
r
ec
asti
n
g
m
eth
o
d
s
,
wh
ich
r
ely
o
n
s
tatio
n
ar
ity
ass
u
m
p
tio
n
s
,
u
s
u
ally
f
ail
wh
en
it
co
m
es
to
r
ea
l
-
wo
r
ld
d
ata
th
at
ex
h
ib
its
s
ig
n
if
ican
t v
ar
iati
o
n
s
[
1
2
]
.
ML
alg
o
r
ith
m
s
h
av
e
b
ee
n
u
s
e
d
m
o
r
e
r
ec
en
tly
to
f
o
r
ec
ast
th
e
Pak
is
tan
GD
P;
th
ese
alg
o
r
ith
m
s
r
ev
ea
l
m
o
r
e
ad
ap
ta
b
ilit
y
th
an
tr
ad
itio
n
al
p
r
ed
ictiv
e
m
o
d
els.
ML
alg
o
r
ith
m
s
ar
e
ca
p
ab
le
o
f
m
ak
in
g
p
r
ed
ictio
n
s
b
ased
o
n
h
is
to
r
ical
d
ata,
in
d
e
p
en
d
e
n
t
o
f
p
r
ec
o
n
ce
iv
ed
n
o
tio
n
s
o
r
j
u
d
g
m
e
n
ts
.
Ov
er
th
e
p
ast
f
ew
d
ec
ad
es,
tech
n
o
lo
g
y
h
as
ad
v
an
ce
d
e
x
p
o
n
en
tially
,
en
ab
lin
g
it
to
h
a
n
d
le
en
o
r
m
o
u
s
am
o
u
n
ts
o
f
d
ata
in
m
illi
s
ec
o
n
d
s
an
d
e
x
tr
ac
t
v
alu
ab
le
in
s
ig
h
ts
f
r
o
m
b
illi
o
n
s
o
f
in
p
u
ts
.
T
h
at
b
ein
g
s
aid
,
t
h
is
tr
en
d
h
as
n
o
t
b
ee
n
f
u
lly
a
d
o
p
ted
b
y
Pak
is
tan
'
s
non
-
f
i
n
an
cial
in
d
u
s
tr
ies.
I
n
Pak
is
tan
,
tr
ad
itio
n
al
tech
n
i
q
u
es su
ch
as b
eta,
s
tan
d
ar
d
d
ev
iatio
n
,
co
n
d
itio
n
al
v
alu
e
at
r
is
k
(
C
Va
R
)
,
an
d
v
alu
e
at
r
is
k
(
VaR)
h
av
e
b
ee
n
th
e
m
ain
s
tay
s
o
f
r
is
k
p
r
ed
ictio
n
.
Ar
tific
ial
in
tellig
en
ce
ap
p
r
o
ac
h
es
h
av
e
n
o
t
b
ee
n
em
p
lo
y
ed
s
ig
n
if
ican
tly
to
ad
d
r
ess
r
is
k
in
th
e
f
in
an
cial
an
d
n
o
n
-
f
in
an
cial
in
d
u
s
tr
ies,
d
esp
ite
th
ese
co
n
v
en
tio
n
al
m
e
th
o
d
s
[
1
3
]
.
T
h
e
r
esear
ch
s
tated
b
y
[
1
4
]
s
h
o
w
th
at
th
e
SVM
ap
p
r
o
ac
h
h
a
s
b
ee
n
ex
ten
s
iv
ely
u
tili
ze
d
to
an
ticip
ate
r
ea
l
GDP
in
m
an
y
r
e
g
io
n
s
o
f
t
h
e
wo
r
ld
in
o
r
d
e
r
to
m
a
k
e
s
wif
t
ec
o
n
o
m
ic
j
u
d
g
m
e
n
t
.
Sev
er
a
l
r
esear
ch
p
r
o
p
o
s
e
f
r
am
ewo
r
k
s
b
ased
o
n
ML
f
o
r
co
o
r
d
i
n
atin
g
th
e
co
o
r
d
in
ate
d
m
an
ag
em
e
n
t
o
f
in
v
e
n
to
r
y
at
ea
ch
n
o
d
e
in
th
e
s
u
p
p
ly
ch
ain
.
T
h
eir
m
et
h
o
d
s
f
in
d
n
ea
r
ly
-
o
p
tim
al
o
r
d
e
r
in
g
s
tr
ateg
ies
b
y
a
p
p
ly
in
g
d
if
f
e
r
en
t
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
Q
-
le
ar
n
in
g
.
k
-
NN
h
as
b
ee
n
u
s
ed
to
f
o
r
ec
ast
h
ea
lth
ca
r
d
d
is
tr
ib
u
tio
n
[
1
5
]
,
tr
af
f
ic
s
p
ee
d
[
1
6
]
,
an
d
s
to
ck
p
r
ice
[
1
7
]
–
[
1
9
]
.
C
o
m
p
ar
ed
to
d
ec
is
io
n
tr
ee
s
a
n
d
Naiv
e
B
ay
es,
k
-
NN
p
r
ed
ictio
n
r
esu
lts
h
av
e
d
em
o
n
s
tr
ated
m
o
r
e
ac
cu
r
ac
y
in
f
o
r
ec
asti
n
g
d
ata
a
n
d
p
r
e
d
ictio
n
s
[
2
0
]
–
[
2
3
]
.
I
n
th
is
s
tu
d
y
,
we
m
o
d
eled
Pak
is
tan
'
s
GDP
g
r
o
wth
r
ate
to
p
r
o
d
u
ce
p
r
elim
in
a
r
y
esti
m
ates
o
f
th
e
cu
r
r
en
t
y
ea
r
ly
GDP
g
r
o
wth
r
ate
an
d
im
m
e
d
i
ate
f
o
r
ec
asts
o
f
th
e
f
o
llo
win
g
y
ea
r
s
'
GDP.
W
e
a
p
p
lied
th
e
m
ea
n
,
n
aïv
e
,
s
ea
s
o
n
al
n
aïv
e
(
SNaiv
e)
,
ex
p
o
n
e
n
tial
s
m
o
o
th
in
g
,
a
n
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tio
n
o
f m
a
ch
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
in
mo
d
ellin
g
a
n
d
fo
r
ec
a
s
tin
g
r
ea
l g
r
o
s
s
…
(
Mo
iz
Qu
r
esh
i
)
1341
k
-
NN
f
o
r
esti
n
g
tech
n
iq
u
es
to
ac
h
iev
e
b
etter
f
o
r
ec
asti
n
g
an
d
ca
p
tu
r
e
d
if
f
er
en
t
p
atter
n
s
an
d
tr
en
d
s
in
th
e
d
ata.
B
y
ap
p
ly
in
g
th
ese
tech
n
iq
u
es,
th
is
s
tu
d
y
aim
s
to
s
u
p
p
o
r
t
p
r
u
d
en
t
ec
o
n
o
m
ic
p
la
n
n
in
g
a
n
d
th
e
d
ev
elo
p
m
en
t
o
f
p
o
licies th
at
im
p
r
o
v
e
th
e
k
e
y
ec
o
n
o
m
ic
in
d
icato
r
s
o
f
t
h
e
Pak
is
tan
i e
co
n
o
m
y
.
T
h
e
aim
o
f
th
is
s
tu
d
y
is
to
ev
alu
ate
th
e
f
o
r
ec
asti
n
g
Pak
is
tan
GDP
p
r
ice
b
y
th
e
n
ew
ap
p
r
o
ac
h
k
-
NN
b
ased
o
n
th
e
ML
to
g
ai
n
th
e
h
ig
h
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
T
h
e
ML
ap
p
r
o
ac
h
h
as
n
o
as
s
u
m
p
tio
n
ab
o
u
t
th
e
s
tatio
n
ar
y
s
er
ies,
s
o
th
e
n
ew
ML
f
o
r
ec
asti
n
g
h
as
p
r
o
v
ed
th
e
o
p
tim
al
f
o
r
ec
asti
n
g
ac
cu
r
a
cy
wh
er
e
th
e
tim
e
s
er
ies
d
ata
is
n
o
n
s
tatio
n
ar
y
s
er
ies.
Usi
n
g
a
n
o
v
el
ML
s
tr
ate
g
y
,
s
p
ec
if
ically
th
e
k
-
NN
al
g
o
r
ith
m
,
th
e
p
u
r
p
o
s
e
o
f
th
is
s
tu
d
y
is
to
as
s
es
s
th
e
ac
cu
r
ac
y
with
wh
ich
GDP
f
o
r
ec
asts
f
o
r
Pak
is
tan
ca
n
b
e
m
ad
e.
T
h
e
n
aiv
e
f
o
r
ec
asti
n
g
m
eth
o
d
,
s
ea
s
o
n
al
n
aiv
e
f
o
r
ec
asti
n
g
m
eth
o
d
,
an
d
s
im
p
le
ex
p
o
n
en
tial
s
m
o
o
th
in
g
m
et
h
o
d
a
r
e
ex
am
p
les
o
f
co
n
v
en
tio
n
al
tim
e
s
er
ies
f
o
r
ec
asti
n
g
tech
n
iq
u
es
th
at
m
a
k
e
ass
u
m
p
tio
n
s
r
eg
a
r
d
in
g
th
e
s
tatio
n
ar
ity
o
f
th
e
tim
e
s
er
ies
d
ata.
T
h
e
s
t
atis
tical
p
r
o
p
er
ties
o
f
th
e
tim
e
s
er
ies,
s
u
ch
as
th
e
m
ea
n
an
d
v
ar
ian
ce
,
m
u
s
t
b
e
s
tatio
n
ar
y
in
o
r
d
e
r
to
b
e
co
n
s
id
er
ed
s
tatio
n
ar
y
.
Ho
wev
er
,
u
n
d
er
ly
i
n
g
tr
en
d
s
,
s
ea
s
o
n
al
ef
f
ec
ts
,
an
d
ab
r
u
p
t
s
tr
u
ctu
r
al
ch
a
n
g
es
f
r
eq
u
e
n
tly
ca
u
s
e
n
o
n
-
s
tatio
n
ar
y
b
eh
av
i
o
r
in
r
ea
l
-
wo
r
ld
ec
o
n
o
m
ic
d
ata,
s
u
ch
as
GDP.
C
las
s
ical
f
o
r
ec
asti
n
g
tech
n
iq
u
es,
wh
ich
eith
er
r
eq
u
ir
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tep
s
to
co
n
v
er
t
n
o
n
-
s
tatio
n
ar
y
d
ata
in
t
o
s
tatio
n
ar
y
f
o
r
m
o
r
r
eq
u
ir
e
d
ata
to
b
e
s
tatio
n
ar
y
,
ca
n
s
u
f
f
er
g
r
ea
tly
f
r
o
m
th
is
in
h
er
e
n
t
n
o
n
-
s
tatio
n
ar
ity
.
I
n
ter
esti
n
g
ly
,
t
h
e
k
-
NN
s
tr
ateg
y
d
o
esn
'
t
f
o
r
ce
s
ev
er
e
p
r
esu
m
p
tio
n
s
ab
o
u
t
th
e
s
tatio
n
ar
ity
o
f
t
h
e
tim
e
s
er
ies
in
f
o
r
m
atio
n
.
k
-
NN
is
a
ty
p
e
o
f
in
s
tan
ce
-
b
ased
lear
n
in
g
th
at
u
s
es
s
im
ilar
itie
s
b
etwe
en
n
ew
an
d
p
r
ev
io
u
s
ly
o
b
s
er
v
ed
in
s
tan
ce
s
to
m
a
k
e
p
r
ed
ictio
n
s
.
k
-
NN
is
ab
le
to
h
an
d
le
n
o
n
-
s
tatio
n
ar
y
d
ata
m
o
r
e
ef
f
ec
tiv
ely
as
a
r
esu
lt
o
f
th
is
ad
ap
tab
ilit
y
,
ca
p
tu
r
in
g
in
tr
icate
p
atter
n
s
an
d
r
elatio
n
s
h
ip
s
with
o
u
t
th
e
n
ee
d
f
o
r
ex
ten
s
iv
e
d
ata
tr
an
s
f
o
r
m
atio
n
o
r
d
etr
e
n
d
in
g
p
r
o
ce
d
u
r
es.
T
o
s
h
o
w
th
e
p
r
e
v
alen
ce
o
f
th
e
k
-
NN
ap
p
r
o
ac
h
f
o
r
d
ete
r
m
in
in
g
Pak
is
tan
's
Gr
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct,
th
is
s
tu
d
y
d
ir
ec
ts
a
r
elativ
e
ex
am
in
atio
n
ag
ai
n
s
t
co
n
v
en
tio
n
al
s
tr
ateg
ies.
T
h
e
d
ataset
u
tili
ze
d
in
co
r
p
o
r
a
tes
au
th
en
tic
Gr
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
f
ig
u
r
es
f
r
o
m
th
e
wo
r
ld
b
an
k
,
wh
ich
ar
e
s
ep
ar
ated
in
to
p
r
ep
ar
in
g
a
n
d
test
in
g
s
ets.
Stan
d
ar
d
m
etr
i
cs
lik
e
r
o
o
t
m
ea
n
s
q
u
ar
e
e
r
r
o
r
(
R
MSE
)
,
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
an
d
m
ea
n
ab
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
PE)
ar
e
u
s
ed
to
e
v
al
u
ate
th
e
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
o
f
th
e
k
-
NN
m
o
d
el
o
n
th
e
test
in
g
s
et.
B
y
q
u
an
tify
in
g
th
e
av
e
r
ag
e
m
ag
n
it
u
d
e
o
f
th
e
f
o
r
ec
ast
er
r
o
r
s
o
n
b
o
th
th
e
ab
s
o
lu
te
a
n
d
r
elativ
e
s
ca
les,
th
ese
m
etr
ics
p
r
o
v
id
e
a
co
m
p
r
eh
e
n
s
iv
e
ev
alu
atio
n
o
f
t
h
e
m
o
d
el'
s
ac
cu
r
ac
y
.
I
n
th
e
r
em
ain
in
g
p
ar
ts
o
f
th
e
p
ap
er
,
s
ec
tio
n
2
d
escr
ib
es
t
h
e
d
ata
an
d
th
e
m
o
d
els
a
p
p
l
ied
,
wh
ile
s
ec
tio
n
3
p
r
esen
ts
th
e
d
ata
an
a
ly
s
is
r
esu
lts
an
d
it
s
d
is
cu
s
s
io
n
s
.
Sectio
n
4
en
d
s
th
e
p
ap
er
wi
th
co
n
clu
s
io
n
s
an
d
r
ec
o
m
m
en
d
atio
n
s
o
f
t
h
e
s
tu
d
y
.
2.
DATA AN
D
M
E
T
H
O
DS
2
.
1
.
Da
t
a
T
h
e
d
ata
u
s
ed
is
Pak
is
tan
'
s
G
DP
g
r
o
wth
r
ate
d
ata
f
r
o
m
1
9
9
0
to
2
0
2
2
f
r
o
m
th
e
W
B
's
o
f
f
icial
web
s
ite
[
1
]
.
T
h
e
s
u
m
m
a
r
y
s
tatis
tics
o
f
o
u
r
d
ata
ar
e
p
r
esen
ted
in
T
ab
le
1
.
T
ab
le
1
s
h
o
ws
th
e
av
er
ag
e
GDP
g
r
o
wth
r
at
e
f
o
r
th
e
p
e
r
io
d
u
n
d
er
c
o
n
s
id
er
atio
n
is
4
.
0
6
7
p
er
ce
n
t
,
wh
ile
th
e
m
in
im
u
m
an
d
m
a
x
im
u
m
GDP
g
r
o
wth
r
ates
wer
e
-
1
.
3
0
0
an
d
7
.
7
1
0
p
er
ce
n
t,
r
esp
ec
tiv
ely
.
T
ab
le
1
.
s
u
m
m
a
r
y
s
tatis
tics
o
f
Pak
is
tan
’
s
GDP
g
r
o
wth
r
ate
f
r
o
m
1
9
9
0
to
2
0
2
2
M
i
n
i
m
u
m
F
i
r
st
q
u
a
r
t
i
l
e
M
e
d
i
a
n
M
e
a
n
Th
i
r
d
q
u
a
r
t
i
l
e
M
a
x
i
m
u
m
S
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
S
k
e
w
n
e
ss
K
u
r
t
o
si
s
-
1
.
3
0
0
2
.
7
0
0
4
.
0
6
7
4
.
0
6
7
5
.
1
2
5
7
.
7
1
0
1
.
9
6
-
0
.
4
3
1
3
.
3
2
4
2
.
2
.
M
et
ho
ds
T
h
is
ar
ticle
esti
m
ates
Pak
i
s
tan
'
s
GDP
f
o
r
th
e
u
p
co
m
in
g
y
ea
r
s
b
y
co
m
p
a
r
in
g
two
d
is
tin
ct
f
o
r
ec
asti
n
g
ap
p
r
o
ac
h
es:
tr
ad
itio
n
al
f
o
r
ec
a
s
tin
g
tech
n
iq
u
es
an
d
ML
m
eth
o
d
s
.
T
h
e
tr
a
d
itio
n
al
m
eth
o
d
s
ap
p
lied
in
clu
d
e
th
e
s
im
p
le
ex
p
o
n
en
tial
s
m
o
o
t
h
in
g
(
SES)
,
SNaiv
e
,
an
d
n
aïv
e
f
o
r
ec
asti
n
g
m
eth
o
d
s
.
Ad
d
itio
n
all
y
,
th
e
s
tu
d
y
u
s
es
k
-
NN
,
a
ML
tech
n
iq
u
e,
to
p
r
e
d
ict
Pak
is
tan
'
s
G
DP.
T
h
ese
m
e
th
o
d
s
ar
e
ev
alu
ated
a
n
d
co
m
p
ar
ed
to
d
eter
m
in
e
th
eir
f
o
r
ec
asti
n
g
ac
c
u
r
ac
y
an
d
s
u
itab
ilit
y
f
o
r
p
r
e
d
ictin
g
f
u
tu
r
e
GDP
g
r
o
wth
.
2
.
2
.
1
.
M
ea
n
f
o
re
ca
s
t
ing
m
e
t
ho
d
T
h
e
m
ea
n
f
o
r
ec
ast
(
MF)
m
eth
o
d
is
a
v
er
y
s
im
p
le
tech
n
iq
u
e
in
wh
ich
all
f
u
tu
r
e
v
alu
es
p
r
ed
icted
ar
e
eq
u
al
to
th
e
p
r
ev
io
u
s
d
ata'
s
a
v
er
ag
e
(
o
r
"m
ea
n
"
)
.
I
f
th
e
d
at
a
s
et
o
f
o
b
s
er
v
atio
n
s
is
d
en
o
ted
b
y
1
,
.
.
.
,
th
en
th
e
f
o
r
ec
ast v
alu
e
is
ca
lcu
lated
b
y
(
1
)
.
+
ℎ
|
=
̅
=
1
+
2
+
3
…
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
3
9
-
1
3
4
9
1342
T
h
e
ar
ith
m
etic
m
ea
n
o
f
all
p
r
io
r
o
b
s
er
v
atio
n
s
,
th
e
m
ea
n
f
o
r
ec
asti
n
g
m
eth
o
d
is
a
clea
r
an
d
ea
s
y
tim
e
s
er
ies
f
o
r
ec
asti
n
g
ap
p
r
o
ac
h
th
at
m
ak
es
p
r
ed
ictio
n
s
f
o
r
t
h
e
u
p
co
m
in
g
tim
e
p
er
io
d
.
Fo
r
s
tatio
n
ar
y
tim
e
s
er
ies
d
ata
th
o
s
e
wh
o
s
e
m
ea
n
d
o
es
n
o
t
ch
an
g
e
o
v
e
r
tim
e
th
is
s
tr
ateg
y
is
esp
ec
ially
h
elp
f
u
l
s
i
n
ce
it
ass
u
m
es
th
at
f
u
tu
r
e
v
alu
es
will
r
o
u
g
h
ly
eq
u
al
th
e
a
v
er
ag
e
o
f
p
ast
v
alu
es.
W
ith
d
ata
th
at
e
x
h
ib
it
tr
en
d
s
o
r
s
ea
s
o
n
al
p
atter
n
s
,
h
o
wev
er
,
its
ef
f
icac
y
is
r
ed
u
c
ed
.
On
e
o
f
its
m
ain
ad
v
an
tag
e
s
is
th
at
it
i
s
s
im
p
le
an
d
ea
s
y
to
ap
p
ly
,
b
u
t
it
m
ay
m
is
s
r
ec
en
t c
h
an
g
es in
th
e
d
at
a
s
in
ce
it c
o
n
s
id
er
s
all
p
r
ev
io
u
s
o
b
s
er
v
atio
n
s
id
en
tically
[
1
9
]
.
2
.
2
.
2
.
Na
iv
e
f
o
re
ca
s
t
ing
m
e
t
ho
d
A
n
aiv
e
f
o
r
ec
ast
(
NF)
en
tails
u
s
in
g
th
e
p
r
ev
io
u
s
o
b
s
er
v
ati
o
n
with
o
u
t
m
o
d
if
icatio
n
as
th
e
b
asis
f
o
r
th
e
f
o
r
ec
ast.
E
s
tim
atin
g
tech
n
iq
u
es
in
wh
ic
h
th
e
p
r
e
v
io
u
s
p
er
io
d
'
s
ac
tu
als
ar
e
u
tili
ze
d
a
s
th
e
f
o
r
ec
ast
f
o
r
a
cu
r
r
en
t p
er
i
o
d
with
o
u
t b
ein
g
a
d
ju
s
ted
o
r
attem
p
tin
g
to
d
eter
m
in
e
ca
u
s
al
elem
en
ts
[
2
1
]
,
[
2
4
]
.
I
t is so
lely
u
s
ef
u
l
f
o
r
co
m
p
ar
is
o
n
with
p
r
o
jectio
n
s
m
ad
e
b
y
m
o
r
e
ad
v
an
ce
d
(
s
o
p
h
is
ticated
)
ap
p
r
o
ac
h
es.
I
t
i
s
f
r
eq
u
en
tly
ca
lled
th
e
p
er
s
is
ten
ce
f
o
r
ec
ast
b
ec
au
s
e
th
e
ea
r
lier
o
b
s
er
v
atio
n
p
er
s
is
ts
.
T
h
is
s
tr
aig
h
tf
o
r
wa
r
d
m
eth
o
d
ca
n
b
e
s
ig
n
if
ican
tly
m
o
d
if
ie
d
f
o
r
s
ea
s
o
n
al
d
ata
[
2
2
]
.
T
h
e
NF is so
m
etim
es k
n
o
wn
as a
r
an
d
o
m
wa
lk
f
o
r
ec
ast s
in
ce
an
NF
is
a
co
r
r
ec
t
a
p
p
r
o
ac
h
w
h
en
d
ata
a
r
a
n
d
o
m
walk
,
an
d
t
h
e
(
)
f
u
n
ctio
n
ca
n
b
e
u
s
ed
in
s
tead
o
f
n
aïv
e
.
T
h
is
is
th
e
b
est
th
at
ca
n
b
e
d
o
n
e
f
o
r
m
an
y
tim
e
s
er
ies,
in
clu
d
in
g
m
o
s
t
s
to
ck
p
r
ice
d
ata,
an
d
ev
e
n
if
it
is
n
o
t
a
g
o
o
d
f
o
r
ec
asti
n
g
m
eth
o
d
,
it p
r
o
v
i
d
e
s
a
u
s
ef
u
l b
en
ch
m
ar
k
f
o
r
o
th
er
f
o
r
ec
asti
n
g
m
eth
o
d
s
.
2
.
2
.
3
.
SNa
iv
e
f
o
re
c
a
s
t
ing
m
e
t
ho
d
I
n
th
e
SNaiv
e
f
o
r
ec
asti
n
g
(
S
NF)
m
eth
o
d
,
we
s
et
th
e
m
o
s
t
cu
r
r
en
t
d
ata
f
r
o
m
th
e
s
am
e
s
e
aso
n
as
th
e
b
aselin
e
f
o
r
ea
ch
esti
m
atio
n
(
e
.
g
.
,
th
e
s
am
e
m
o
n
th
o
f
th
e
p
r
e
v
io
u
s
y
ea
r
)
[
2
5
]
.
T
h
e
esti
m
ate
f
o
r
th
e
tim
e
+
ℎ
is
wr
itten
as
(
2
)
.
+
ℎ
|
=
+
ℎ
−
(
+
1
)
(
2
)
w
h
er
e
is
th
e
s
ea
s
o
n
al
p
er
io
d
o
r
tim
e,
an
d
is
th
e
in
teg
e
r
p
a
r
t
o
f
ℎ
−
1
.
I
t
s
ee
m
s
m
o
r
e
d
if
f
icu
lt
th
an
it
is
.
Fo
r
in
s
tan
ce
,
wh
en
u
s
in
g
m
o
n
th
ly
d
ata,
th
e
f
o
r
ec
ast
f
o
r
all
u
p
co
m
in
g
Feb
r
u
ar
y
v
alu
es
is
th
e
s
am
e
as
th
e
m
o
s
t
r
ec
en
t
Feb
r
u
a
r
y
v
al
u
e
r
ec
o
r
d
e
d
.
W
h
en
u
s
in
g
q
u
a
r
ter
ly
d
ata,
th
e
p
r
e
d
ictio
n
o
f
all
u
p
c
o
m
in
g
Q2
v
alu
es
e
q
u
als
th
e
m
o
s
t
r
ec
en
t
Q2
v
alu
e
o
b
s
er
v
ed
(
wh
e
r
e
Q2
m
ea
n
s
th
e
s
ec
o
n
d
q
u
ar
ter
)
.
T
h
e
s
am
e
r
u
l
es
wo
u
ld
ap
p
ly
f
o
r
ad
d
itio
n
al
m
o
n
th
s
,
q
u
a
r
ter
s
,
a
n
d
o
th
e
r
s
ea
s
o
n
al
tim
es
[
2
5
]
.
2
.
2
.
4
.
Sim
ple
ex
po
nentia
l sm
o
o
t
hin
g
m
et
ho
d
Gen
er
ally
,
th
e
s
im
p
le
ex
p
o
n
e
n
tial
s
m
o
o
th
in
g
(
SES)
m
o
d
el
is
p
r
ed
icate
d
o
n
th
e
i
d
ea
th
at
a
tim
e
s
er
ies
'
lev
el
s
h
o
u
ld
o
s
cillate
ar
o
u
n
d
a
s
et
lev
el
o
r
f
lu
ctu
at
e
ar
o
u
n
d
a
co
n
s
tan
t
lev
el
[
2
3
]
,
[
2
6
]
,
[
2
7
]
.
T
h
e
f
o
llo
win
g
eq
u
atio
n
g
iv
es th
e
S
E
S m
o
d
el:
(
)
=
(
)
+
(
)
(
3
)
w
h
er
e
(
)
tak
es a
co
n
s
tan
t a
t th
e
tim
e
an
d
m
a
y
ch
an
g
e
s
lo
wly
o
v
er
tim
e;
(
)
is
a
r
an
d
o
m
v
a
r
iab
le
u
s
ed
to
d
escr
ib
e
th
e
ef
f
ec
t
o
f
s
to
ch
asti
c
f
lu
ctu
atio
n
.
2
.
2
.
5
.
k
-
NN
m
et
ho
d
C
o
m
p
u
tatio
n
al
in
tellig
en
ce
a
n
d
o
th
er
ML
m
et
h
o
d
s
[
2
8
]
in
f
o
r
ec
asti
n
g
tim
e
s
er
ies
h
av
e
b
ee
n
in
cr
ea
s
in
g
ly
co
m
m
o
n
in
r
ec
e
n
t
d
ec
ad
es.
T
w
o
in
tr
ig
u
in
g
f
e
atu
r
es
o
f
c
o
m
p
u
tatio
n
al
in
tellig
en
ce
an
d
ML
t
h
at
d
is
tin
g
u
is
h
th
em
f
r
o
m
co
n
v
en
tio
n
al
s
tatis
tical
m
o
d
els
ar
e
n
o
n
lin
ea
r
ity
an
d
th
e
lac
k
o
f
a
n
u
n
d
er
ly
in
g
m
o
d
el
(
also
k
n
o
wn
as
n
o
n
-
p
ar
am
et
r
icity
)
[
2
9
]
.
T
h
e
k
-
NN
r
eg
r
ess
io
n
-
b
ased
ap
p
r
o
ac
h
is
n
o
n
-
p
a
r
am
etr
ic
an
d
r
e
q
u
ir
es
n
o
p
r
io
r
ass
u
m
p
tio
n
s
ab
o
u
t
t
h
e
n
atu
r
e
o
f
th
e
d
ata
[
3
0
]
.
I
t
s
ab
ilit
y
to
lear
n
co
m
p
licated
f
u
n
ctio
n
s
f
ast
an
d
ac
cu
r
ately
is
its
m
ain
b
en
ef
it.
T
h
e
f
o
llo
win
g
̂
r
esu
lt f
o
r
a
g
iv
en
f
r
o
m
th
e
tr
ain
i
n
g
d
ata
is
o
b
tain
ed
b
y
tak
in
g
th
e
m
ea
n
o
f
th
e
r
esp
o
n
s
es
to
th
ese
in
d
ep
en
d
en
t
v
ar
ia
b
les,
tak
in
g
in
to
ac
c
o
u
n
t
tr
ain
in
g
d
ata
o
b
s
er
v
atio
n
s
with
clo
s
e
to
:
̂
(
)
=
1
∑
1
1
(
4
)
wh
er
e
N
s
tan
d
s
f
o
r
th
e
k
s
p
o
ts
th
at
ar
e
clo
s
est.
I
n
ac
tu
ality
,
a
wid
e
v
ar
iety
o
f
d
is
tan
ce
m
e
asu
r
es
m
ay
b
e
u
s
ed
to
ass
ess
th
e
p
r
o
x
im
ity
o
f
tw
o
s
ites
.
I
n
p
ar
ticu
lar
,
t
h
e
E
u
c
lid
ea
n
d
is
tan
ce
was
ap
p
lied
i
n
o
u
r
wo
r
k
.
Giv
in
g
d
if
f
er
en
t
f
ac
to
r
s
in
th
e
n
ea
r
r
eg
io
n
v
ar
y
in
g
d
e
g
r
ee
s
o
f
weig
h
t
is
b
en
ef
icial.
W
e
e
m
p
lo
y
ed
a
d
e
n
s
ity
d
is
tr
ib
u
tio
n
b
ased
o
n
th
e
Ga
u
s
s
ian
d
is
tr
ib
u
tio
n
as th
ese
d
ata
ar
e
co
m
p
u
ted
u
s
in
g
a
d
en
s
ity
f
u
n
ctio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tio
n
o
f m
a
ch
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
in
mo
d
ellin
g
a
n
d
fo
r
ec
a
s
tin
g
r
ea
l g
r
o
s
s
…
(
Mo
iz
Qu
r
esh
i
)
1343
W
e
em
p
lo
y
ed
t
h
e
f
o
llo
win
g
p
er
f
o
r
m
a
n
ce
m
etr
ics
to
ev
alu
at
e
ea
ch
f
o
r
ec
asti
n
g
m
o
d
el'
s
p
er
f
o
r
m
an
ce
:
MA
PE,
MA
E
,
an
d
R
M
SE
[
3
0
]
–
[
3
2
]
.
T
h
e
f
o
llo
win
g
is
an
ex
p
lan
atio
n
o
f
th
ese
m
ea
s
u
r
em
en
ts
:
o
b
s
er
v
e
d
v
alu
es
an
d
esti
m
ated
o
r
an
tic
ip
ated
v
alu
es
a
r
e
s
h
o
wn
.
T
h
e
av
er
ag
e
o
f
all
ab
s
o
lu
te
er
r
o
r
s
is
k
n
o
wn
as
th
e
MA
E
[
3
3
]
.
T
h
e
f
o
llo
win
g
f
o
r
m
u
la
is
ex
p
r
ess
ed
as:
MAE
=
1
∑
|
=
1
−
̂
|
(
5
)
R
MSE
is
a
wid
ely
u
tili
ze
d
m
etr
ic
am
o
n
g
p
r
ac
titi
o
n
er
s
an
d
ac
a
d
em
ics
f
o
r
e
v
alu
atin
g
th
e
p
r
ec
is
io
n
o
f
f
o
r
ec
asti
n
g
m
o
d
els.
R
MSE
q
u
an
tifie
s
th
e
d
is
p
ar
ity
b
etwe
en
o
b
s
er
v
ed
a
n
d
e
x
p
ec
ted
v
alu
es
,
d
eter
m
in
e
d
u
s
in
g
th
e
s
u
b
s
eq
u
en
t f
o
r
m
u
la
.
=
√
1
∑
(
−
̂
=
1
)
2
(
6
)
T
h
e
MA
PE
is
th
e
m
ea
n
o
r
av
er
ag
e
o
f
th
e
ab
s
o
lu
te
p
e
r
ce
n
t
ag
e
er
r
o
r
s
o
f
f
o
r
ec
asts
[
3
4
]
,
[
3
5
]
.
T
h
e
d
if
f
er
e
n
ce
b
etwe
en
th
e
p
r
ed
icted
an
d
ac
tu
al
v
alu
es
is
ca
lled
th
e
er
r
o
r
.
T
o
ca
lcu
late
MA
PE,
th
e
p
e
r
ce
n
tag
e
e
r
r
o
r
s
ar
e
ad
d
ed
to
g
eth
er
r
eg
ar
d
less
o
f
t
h
eir
s
ig
n
.
T
h
e
f
o
llo
win
g
f
o
r
m
u
la
ex
p
r
ess
es it:
=
1
∑
|
−
̂
=
0
|
×
100
(
7
)
Fro
m
2
0
2
3
th
r
o
u
g
h
2
0
2
6
,
p
r
ed
ictio
n
s
will
b
e
b
ased
o
n
th
e
b
est
-
s
elec
ted
m
o
d
el,
wh
ich
will
b
e
d
eter
m
in
ed
u
s
in
g
th
e
R
MSE
,
MA
E
,
an
d
MA
PE
cr
iter
ia.
All
m
o
d
els
wer
e
an
aly
ze
d
i
n
u
s
in
g
th
e
p
ac
k
ag
es
(
)
,
(
)
,
(
)
,
(
)
,
(
)
,
an
d
(
)
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Pre
d
ictin
g
Pak
is
tan
'
s
an
n
u
al
GDP
g
r
o
wth
r
ate
is
cr
u
cial
f
o
r
s
tr
ateg
ic
ec
o
n
o
m
ic
p
la
n
n
in
g
an
d
p
o
lic
y
f
o
r
m
u
latio
n
.
I
n
th
is
s
tu
d
y
,
we
ass
es
s
s
ev
er
al
f
o
r
ec
asti
n
g
m
eth
o
d
s
u
s
in
g
an
in
-
s
am
p
le
d
atas
et
to
d
eter
m
in
e
th
e
m
o
s
t
ac
cu
r
ate
a
p
p
r
o
ac
h
.
Am
o
n
g
t
h
e
m
o
d
els
ev
alu
ated
ar
e
tr
ad
itio
n
al
tim
e
s
er
ies
m
eth
o
d
s
lik
e
t
h
e
m
ea
n
f
o
r
ec
ast
m
eth
o
d
,
n
aïv
e
f
o
r
ec
a
s
t
m
eth
o
d
,
s
ea
s
o
n
al
n
aïv
e
f
o
r
e
ca
s
t
m
eth
o
d
,
a
n
d
s
ea
s
o
n
al
e
x
p
o
n
en
tial
s
m
o
o
th
i
n
g
m
eth
o
d
,
alo
n
g
s
id
e
th
e
m
o
d
er
n
ML
tech
n
iq
u
e
k
n
o
wn
as
th
e
k
-
NN
ap
p
r
o
ac
h
.
T
h
e
e
v
alu
atio
n
cr
iter
ia
f
o
c
u
s
o
n
m
etr
ics
s
u
ch
as
R
MSE
,
MA
E
,
an
d
MA
PE,
wh
ich
a
r
e
ess
en
tial
f
o
r
g
a
u
g
in
g
p
r
e
d
ictiv
e
ac
c
u
r
ac
y
.
Ou
r
f
in
d
in
g
s
r
ev
ea
l
th
at
th
e
k
-
NN
m
eth
o
d
c
o
n
s
is
ten
tly
ac
h
iev
es
th
e
lo
west
er
r
o
r
r
ates
ac
r
o
s
s
th
ese
m
etr
ics
co
m
p
ar
ed
t
o
th
e
class
ical
tim
e
s
er
ies
m
o
d
els.
T
h
is
s
u
p
er
io
r
p
er
f
o
r
m
a
n
ce
u
n
d
e
r
s
co
r
es
th
e
k
-
NN
a
p
p
r
o
ac
h
as
th
e
o
p
tim
al
ch
o
ice
f
o
r
f
o
r
ec
asti
n
g
Pak
is
tan
'
s
GD
P
g
r
o
wth
r
ate,
o
f
f
er
in
g
r
o
b
u
s
t
an
d
r
eliab
le
p
r
e
d
ictio
n
s
th
at
o
u
tp
ac
e
tr
a
d
itio
n
al
f
o
r
ec
asti
n
g
m
eth
o
d
s
.
T
h
e
co
m
p
r
eh
en
s
iv
e
a
n
aly
s
is
en
s
u
r
es
a
r
ig
o
r
o
u
s
co
m
p
a
r
is
o
n
,
p
r
o
v
i
d
in
g
in
s
ig
h
ts
in
to
th
e
ef
f
ec
tiv
en
ess
o
f
co
n
tem
p
o
r
ar
y
ML
tech
n
iq
u
es in
ec
o
n
o
m
ic
f
o
r
ec
asti
n
g
co
n
te
x
ts
.
T
ab
le
2
ev
alu
ates
t
h
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
e
n
t
ap
p
r
o
ac
h
e
s
to
f
o
r
ec
asti
n
g
Pak
is
tan
'
s
an
n
u
al
GDP
g
r
o
wth
r
ate
u
s
in
g
th
e
in
-
s
am
p
l
e
d
ata
s
et.
I
t is ev
id
en
t th
at
th
e
R
MSE
,
MA
E
,
an
d
MA
PE
v
alu
es o
n
th
e
b
asis
o
f
th
e
k
-
NN
tech
n
i
q
u
e
h
a
d
th
e
m
in
im
u
m
th
a
n
all
o
th
er
f
o
u
r
-
tim
e
s
er
ies
m
o
d
els
s
u
ch
as
th
e
m
ea
n
f
o
r
ec
ast
m
eth
o
d
,
n
aï
v
e
f
o
r
ec
ast
m
eth
o
d
,
s
ea
s
o
n
al
n
aïv
e
f
o
r
ec
ast
m
et
h
o
d
,
an
d
s
ea
s
o
n
al
ex
p
o
n
en
tial
s
m
o
o
th
in
g
m
eth
o
d
.
T
h
er
ef
o
r
e,
th
e
k
-
NN
m
eth
o
d
is
co
n
s
id
er
ed
th
e
b
est
m
eth
o
d
f
o
r
f
o
r
esti
n
g
th
e
an
n
u
al
Pak
is
tan
g
r
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
g
r
o
wth
r
ate.
T
h
e
n
,
all
o
th
er
class
ical
tim
e
s
e
r
ies
ap
p
r
o
ac
h
es
ar
e
b
ased
o
n
th
e
d
ata
u
n
d
e
r
co
n
s
id
er
atio
n
.
T
ab
le
2
.
E
v
alu
atio
n
o
f
th
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
en
t m
et
h
o
d
s
in
p
r
ed
ictin
g
Pak
is
tan
’
s
an
n
u
al
GDP
g
r
o
wth
r
ate
M
e
t
h
o
d
s
R
M
S
E
M
A
E
M
A
P
E
M
e
a
n
1
.
8
9
6
1
.
4
9
3
5
6
.
9
0
1
N
a
ï
v
e
2
.
4
4
3
1
.
8
1
7
6
9
.
0
0
7
S
N
a
i
v
e
2
.
8
3
8
2
.
1
8
5
8
3
.
7
1
1
Ex
p
o
n
e
n
t
i
a
l
s
m
o
o
t
h
i
n
g
1
.
8
9
6
1
.
4
9
3
5
6
.
9
0
4
k
-
N
N
f
o
r
e
c
a
s
t
me
t
h
o
d
1
.
7
0
4
1
.
2
7
0
4
2
.
0
5
0
T
h
e
s
im
ilar
ex
h
i
b
itio
n
o
f
d
if
f
er
en
t
an
ticip
atin
g
m
eth
o
d
s
f
o
r
an
ticip
atin
g
Pak
is
tan
'
s
y
ea
r
ly
GDP
d
ev
elo
p
m
e
n
t
r
ate,
in
v
o
lv
in
g
in
-
ex
am
p
le
in
f
o
r
m
atio
n
.
R
MS
E
,
MA
E
,
an
d
MA
PE
ar
e
th
e
ev
alu
atio
n
cr
iter
ia.
T
ab
le
2
g
i
v
es
an
item
ized
a
s
s
es
s
m
en
t
o
f
th
e
p
r
esen
tatio
n
o
f
v
ar
io
u
s
g
au
g
i
n
g
tech
n
i
q
u
es
f
o
r
f
o
r
eseein
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
3
9
-
1
3
4
9
1344
Pak
is
tan
's
y
ea
r
ly
g
r
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
d
e
v
elo
p
m
e
n
t
r
ate,
in
v
o
lv
in
g
in
-
ex
am
p
le
in
f
o
r
m
a
tio
n
.
R
MSE
,
MA
E
,
an
d
MA
PE
ar
e
th
e
e
v
alu
atio
n
m
etr
ics.
T
h
e
f
o
r
ec
asti
n
g
m
o
d
els'
ac
cu
r
ac
y
ca
n
o
n
ly
b
e
ev
alu
ated
u
s
in
g
th
ese
m
etr
ics,
with
lo
wer
v
alu
es
i
n
d
icatin
g
b
etter
p
er
f
o
r
m
a
n
ce
.
W
h
en
c
o
m
p
ar
e
d
t
o
th
e
o
th
e
r
a
p
p
r
o
ac
h
es,
th
e
k
-
NN
m
eth
o
d
p
er
f
o
r
m
ed
th
e
b
est
an
d
h
ad
th
e
l
o
west
R
MSE
,
MA
E
,
an
d
MA
PE
v
alu
es.
T
h
is
s
u
g
g
ests
th
at
f
o
r
ec
asts
m
ad
e
u
s
in
g
th
e
k
-
NN
m
eth
o
d
ar
e
m
o
r
e
a
cc
u
r
ate
an
d
tr
u
s
tw
o
r
th
y
.
T
h
e
m
ea
n
f
o
r
e
ca
s
t
m
eth
o
d
,
n
av
e
f
o
r
ec
ast
m
eth
o
d
,
s
ea
s
o
n
al
n
av
e
f
o
r
ec
a
s
t
m
eth
o
d
,
an
d
s
ea
s
o
n
al
ex
p
o
n
en
tial
s
m
o
o
th
in
g
m
et
h
o
d
,
o
n
th
e
o
t
h
er
h
an
d
,
h
a
d
h
ig
h
er
er
r
o
r
r
ates.
T
h
ese
tr
ad
itio
n
al
tech
n
iq
u
es
f
r
eq
u
e
n
tly
d
ep
en
d
o
n
p
r
esu
m
p
tio
n
s
ab
o
u
t
in
f
o
r
m
atio
n
,
f
o
r
ex
am
p
le,
s
tatio
n
ar
ity
,
wh
ich
m
ay
n
o
t
h
o
ld
in
ce
r
tifia
b
le
f
in
an
cial
in
f
o
r
m
atio
n
p
o
r
t
r
ay
ed
b
y
v
ac
illatio
n
s
an
d
non
-
d
ir
ec
t
p
atter
n
s
.
T
h
e
k
-
N
N
m
eth
o
d
'
s
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
ca
n
b
e
attr
ib
u
ted
t
o
its
ab
ilit
y
to
id
en
tif
y
in
tr
icate
d
ata
p
atter
n
s
with
o
u
t
r
ely
in
g
o
n
p
r
esu
m
p
tio
n
s
ab
o
u
t
th
e
d
ata'
s
d
is
tr
ib
u
tio
n
o
r
s
tatio
n
ar
ity
.
k
-
NN'
s
ab
ilit
y
to
h
a
n
d
le
n
o
n
-
lin
ea
r
ti
m
e
s
er
ies
d
ata,
wh
ich
is
co
m
m
o
n
in
ec
o
n
o
m
ic
f
o
r
ec
asti
n
g
,
is
en
h
an
ce
d
b
y
its
f
lex
ib
ilit
y
.
B
y
s
h
o
win
g
lo
we
r
m
is
tak
e
m
ea
s
u
r
em
en
ts
,
th
e
k
-
NN
ap
p
r
o
ac
h
en
d
s
u
p
b
e
in
g
m
o
r
e
ex
ac
t
in
d
eter
m
in
in
g
Pak
is
tan
'
s
GDP
d
ev
elo
p
m
e
n
t
r
ate.
Po
licy
m
ak
er
s
an
d
ec
o
n
o
m
ic
p
la
n
n
er
s
w
h
o
r
el
y
o
n
p
r
ec
is
e
f
o
r
ec
asts
to
m
a
k
e
i
n
f
o
r
m
ed
d
ec
is
io
n
s
n
ee
d
t
h
is
im
p
r
o
v
ed
a
cc
u
r
ac
y
.
T
h
e
d
is
co
v
er
ies
f
ea
t
u
r
e
th
e
ca
p
a
b
ilit
y
o
f
AI
p
r
o
ce
d
u
r
es
lik
e
k
-
NN
i
n
u
p
g
r
a
d
in
g
g
au
g
in
g
ex
ac
t
n
ess
an
d
g
iv
in
g
m
o
r
e
s
o
lid
f
in
an
cial
f
o
r
ec
asts
,
ev
en
tu
ally
s
u
p
p
o
r
tin
g
b
etter
m
o
n
etar
y
p
r
ep
ar
atio
n
an
d
s
tr
ateg
y
d
etailin
g
.
Fig
u
r
e
1
s
h
o
ws
Pak
is
tan
'
s
An
n
u
al
GDP
g
r
o
wth
r
ate
f
r
o
m
1
9
9
0
t
o
2
0
2
2
.
I
n
1
9
9
0
,
Pak
is
tan
’
s
GDP
g
r
o
wth
r
ate
was
4
.
5
p
er
ce
n
t
,
wh
ich
in
cr
ea
s
ed
to
a
p
ea
k
o
f
7
.
7
p
er
ce
n
t
in
two
y
ea
r
s
.
So
m
e
f
lu
ctu
atio
n
s
wer
e
f
o
u
n
d
in
t
h
e
GDP
g
r
o
wth
r
ate
f
r
o
m
2
0
1
9
t
o
2
0
2
0
.
Ho
wev
er
,
alm
o
s
t
all
in
ter
n
atio
n
al
m
ar
k
ets
cr
ash
ed
d
u
e
to
th
e
C
OVI
D
-
1
9
p
an
d
em
ic;
th
e
GDP
g
r
o
wth
r
ate
d
ec
r
ea
s
ed
to
a
r
ec
o
r
d
lo
w
o
f
-
1
.
3
0
p
er
ce
n
t
.
Usi
n
g
9
5
p
er
ce
n
t
o
f
o
u
r
d
ata
f
o
r
test
in
g
an
d
5
p
er
ce
n
t
f
o
r
f
o
r
ec
asti
n
g
,
we
p
r
esen
t
th
e
f
o
r
ec
ast
f
o
r
all
m
o
d
els
d
is
cu
s
s
ed
in
th
e
m
eth
o
d
s
s
ec
tio
n
[
3
6
]
–
[
3
8
]
.
Fig
u
r
e
2
s
h
o
ws
th
e
an
n
u
al
Pak
i
s
tan
GDP
g
r
o
wth
r
ate
f
o
r
ec
ast
b
y
th
e
MF,
u
p
o
n
wh
ich
we
f
in
d
th
e
f
o
r
ec
ast
wit
h
ce
n
tr
o
id
v
al
u
e
in
th
e
d
ata
s
et.
Fo
r
ec
asts
f
o
r
all
p
r
ed
icted
p
r
ices
eq
u
al
th
e
p
ast
d
ata'
s
av
er
ag
e
(
o
r
m
ea
n
)
.
Fig
u
r
e
3
s
h
o
ws
th
e
f
o
r
ec
ast
f
o
r
NM
.
I
t
is
wo
r
th
n
o
tin
g
th
at
th
e
NF
ca
n
b
e
im
p
lem
en
ted
in
a
n
am
esak
e
f
u
n
ctio
n
.
T
h
e
n
aiv
e
m
eth
o
d
f
o
r
e
ca
s
ted
v
alu
es
f
o
r
Pak
is
tan
'
s
G
DP
g
r
o
wth
r
ate
f
o
r
th
e
co
m
in
g
y
ea
r
s
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
,
ar
e
th
e
s
am
e
at
4
.
0
p
e
r
ce
n
t.
In
Fig
u
r
e
4
,
th
e
ex
p
o
n
e
n
tial
s
m
o
o
th
in
g
m
eth
o
d
(
E
SS
)
m
eth
o
d
f
o
r
ec
ast
v
alu
es
f
o
r
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
wer
e
all
th
e
s
am
e,
ju
s
t
as
in
th
e
ca
s
e
o
f
th
e
NF.
I
n
T
ab
le
3
,
we
p
r
esen
t
th
e
d
if
f
er
en
t
f
o
r
ec
asti
n
g
m
eth
o
d
s
an
d
t
h
eir
r
esp
ec
tiv
e
Pak
is
tan
's
g
r
o
s
s
d
o
m
esti
c
(
GDP)
g
r
o
wth
r
ate
p
r
ed
ictio
n
s
f
o
r
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
f
o
r
all
m
o
d
els
u
n
d
er
co
n
s
id
er
atio
n
.
T
h
e
MF
f
o
r
ec
asted
Pak
is
tan
'
s
G
DP
g
r
o
wth
r
ate
f
o
r
t
h
e
u
p
co
m
in
g
y
ea
r
s
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
a
n
d
2
0
2
6
at
4
.
0
6
5
p
er
c
en
t,
r
esp
ec
tiv
ely
.
I
n
co
n
tr
ast,
th
e
E
SS
f
o
r
ec
asted
Pak
is
tan
's GD
P g
r
o
wth
r
ate
f
o
r
th
e
y
ea
r
s
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
at
4
.
0
6
4
p
er
ce
n
t,
r
esp
ec
tiv
ely
.
T
h
e
SNF
f
o
r
ec
asted
v
alu
es
ar
e
6
.
0
0
0
,
4
.
0
0
0
,
4
.
0
5
0
,
an
d
6
.
0
1
0
f
o
r
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
,
r
e
s
p
ec
tiv
ely
.
Fo
r
th
e
NF,
th
e
f
o
r
ec
asted
v
alu
es
f
o
r
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
wer
e
4
.
0
0
0
p
er
ce
n
t,
r
esp
ec
ti
v
ely
.
Usi
n
g
th
e
ML
tech
n
iq
u
e,
k
-
NN'
s
f
o
r
ec
ast
v
alu
e
f
o
r
2
0
2
3
is
3
.
7
6
3
,
wh
ile
th
at
o
f
2
0
2
4
,
2
0
2
5
,
a
n
d
2
0
2
6
ar
e
3
.
3
4
8
,
4
.
7
4
1
,
an
d
5
.
9
2
0
p
er
ce
n
t,
r
esp
ec
tiv
ely
.
Fig
u
r
e
5
s
h
o
ws
th
e
f
o
r
ec
aste
d
v
alu
es
o
f
Pak
is
tan
’
s
GDP
g
r
o
wth
d
ata
b
y
t
h
e
k
-
NN.
W
e
ca
n
o
b
s
er
v
e
a
f
lu
c
tu
atin
g
f
o
r
ec
ast
s
im
ilar
to
t
h
e
o
r
ig
in
al
s
er
ies.
T
h
is
s
h
o
ws
t
h
at
Pak
is
tan
’
s
GDP
g
r
o
wth
r
ate
will c
o
n
tin
u
e
to
d
ec
r
ea
s
e
in
2
0
2
3
an
d
in
cr
ea
s
e
t
h
er
ea
f
ter
,
a
f
ter
wh
ich
it will d
ec
r
ea
s
e
ag
ain
.
Fig
u
r
e
1
.
An
n
u
al
Pak
is
tan
GDP
g
r
o
wth
r
ate
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tio
n
o
f m
a
ch
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
in
mo
d
ellin
g
a
n
d
fo
r
ec
a
s
tin
g
r
ea
l g
r
o
s
s
…
(
Mo
iz
Qu
r
esh
i
)
1345
Fig
u
r
e
2
.
Fo
r
ec
ast GDP
b
y
t
h
e
MF
Fig
u
r
e
3
.
Fo
r
ec
ast v
al
u
es Pak
is
tan
(
GDP)
Gr
o
wth
R
ate
f
r
o
m
NF
Fig
u
r
e
4
.
Fo
r
ec
ast v
al
u
es Pak
is
tan
(
GDP)
g
r
o
wth
r
ate
f
r
o
m
SES
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
3
9
-
1
3
4
9
1346
T
ab
le
3
.
T
h
e
f
o
r
ec
asted
v
alu
e
o
f
GDP
d
ata
o
f
Pak
is
tan
u
s
in
g
f
o
r
ec
asti
n
g
m
eth
o
d
o
l
o
g
y
M
e
t
h
o
d
s
Y
e
a
r
F
o
r
e
c
a
st
v
a
l
u
e
Lo
8
0
H
i
8
0
Lo
9
5
H
i
9
5
M
e
a
n
f
o
r
e
c
a
s
t
2
0
2
3
4
.
0
6
5
1
.
5
0
7
6
.
6
2
3
0
.
0
8
3
8
.
0
4
7
2
0
2
4
4
.
0
6
5
1
.
5
0
7
6
.
6
2
3
0
.
0
8
3
8
.
0
4
7
2
0
2
5
4
.
0
6
5
1
.
5
0
7
6
.
6
2
3
0
.
0
8
3
8
.
0
4
7
2
0
2
6
4
.
0
6
5
1
.
5
0
7
6
.
6
2
3
0
.
0
8
3
8
.
0
4
7
N
a
ï
v
e
f
o
r
e
c
a
st
2
0
2
3
4
.
0
0
0
0
.
8
6
8
7
.
1
3
1
-
0
.
7
8
8
8
.
7
8
8
2
0
2
4
4
.
0
0
0
-
0
.
4
2
7
8
.
4
2
7
-
2
.
7
7
1
1
0
.
7
7
1
2
0
2
5
4
.
0
0
0
-
1
.
4
2
3
9
.
4
2
3
-
4
.
2
9
3
1
2
.
2
9
3
2
0
2
6
4
.
0
0
0
-
2
.
2
6
2
1
0
.
2
6
2
-
5
.
5
7
6
1
3
.
5
7
6
S
N
F
2
0
2
3
6
.
0
0
0
2
.
3
6
1
9
.
6
3
8
0
.
4
3
6
1
1
.
5
6
3
2
0
2
4
4
.
0
0
0
0
.
3
6
1
7
.
6
3
8
-
1
.
5
6
3
9
.
5
6
3
2
0
2
5
4
.
0
5
0
-
1
.
1
4
4
9
.
1
4
4
-
3
.
8
6
8
1
1
.
8
6
8
2
0
2
6
6
.
0
1
0
-
0
.
3
0
1
1
2
.
3
0
1
-
3
.
6
3
6
1
5
.
0
2
3
Ex
p
o
n
e
n
t
i
a
l
sm
o
o
t
h
i
n
g
f
o
r
e
c
a
st
2
0
2
3
4
.
0
6
4
1
.
5
5
7
6
.
5
7
2
0
.
2
2
9
7
.
9
0
0
2
0
2
4
4
.
0
6
4
1
.
5
5
7
6
.
5
7
2
0
.
2
2
9
7
.
9
0
0
2
0
2
5
4
.
0
6
4
1
.
5
5
7
6
.
5
7
2
0
.
2
2
9
7
.
9
0
0
2
0
2
6
4
.
0
6
4
1
.
5
5
7
6
.
5
7
2
0
.
2
2
9
7
.
9
0
0
k
-
NN
b
a
se
f
o
r
e
c
a
st
2
0
2
3
3
.
7
6
3
1
.
3
2
1
3
.
9
8
1
0
.
0
6
7
7
.
3
4
5
2
0
2
4
3
.
3
4
8
1
.
4
5
6
3
.
9
4
1
0
.
0
7
6
7
.
4
3
2
2
0
2
5
4
.
7
4
1
1
.
3
1
2
3
.
9
1
2
0
.
0
7
8
7
.
8
7
6
2
0
2
6
5
.
9
2
0
1
.
3
4
5
3
.
9
1
6
0
.
0
8
9
7
.
3
7
6
Fig
u
r
e
5
.
Fo
r
ec
ast v
al
u
es Pak
is
tan
(
GDP)
g
r
o
wth
r
ate
f
r
o
m
k
-
NN
4.
CO
NCLU
SI
O
N
I
n
th
is
r
esear
ch
,
v
ar
io
u
s
f
o
r
e
ca
s
tin
g
tech
n
iq
u
es
is
u
s
ed
s
u
ch
as`th
e
MF,
NF,
S
NF,
E
S
S,
an
d
th
e
m
o
d
er
n
k
-
NN
ap
p
r
o
ac
h
wer
e
ev
alu
ated
u
s
in
g
d
ata
s
o
u
r
ce
d
f
r
o
m
th
e
W
o
r
ld
B
an
k
.
T
h
e
ev
alu
atio
n
c
r
iter
ia,
co
m
p
r
is
in
g
R
MSE
,
MA
PE,
an
d
MA
E
,
wer
e
em
p
l
o
y
ed
to
d
eter
m
in
e
th
e
m
o
s
t
ac
cu
r
ate
f
o
r
ec
asti
n
g
m
eth
o
d
.
Am
o
n
g
th
ese
tech
n
iq
u
es,
th
e
ML
m
o
d
el
is
m
o
r
e
s
u
p
p
o
r
tiv
e
in
th
e
ca
s
e
o
f
n
o
n
lin
er
tim
e
s
er
ies d
ata.
T
h
e
k
-
NN
m
eth
o
d
is
u
s
ed
to
f
o
r
ec
asti
n
g
th
e
Pak
is
tan
GDP.
T
h
is
m
eth
o
d
is
m
o
r
e
o
p
tim
al
r
esu
lt
b
ec
au
s
e
it
f
o
r
e
cas
t
th
e
n
ex
t
v
alu
e
o
r
th
e
o
n
e
h
ea
d
f
o
r
e
ca
s
t
v
alu
e
o
n
th
e
b
ase
o
f
k
-
NN
alg
o
r
ith
m
.
T
h
e
k
-
NN
m
eth
o
d
em
er
g
ed
as
th
e
o
p
tim
al
ch
o
ice,
c
o
n
s
is
ten
tly
d
em
o
n
s
tr
atin
g
s
u
p
er
i
o
r
p
e
r
f
o
r
m
an
ce
ac
r
o
s
s
all
ev
alu
atio
n
m
et
r
ics.
Sp
ec
if
ically
,
it
ex
h
ib
ited
th
e
lo
west
R
MSE
,
MA
PE,
an
d
MA
E
v
alu
es
c
o
m
p
ar
ed
to
th
e
tr
ad
itio
n
al
ti
m
e
s
er
ies
m
eth
o
d
s
ev
alu
ated
.
T
h
is
in
d
icate
s
th
at
th
e
k
-
NN
ap
p
r
o
ac
h
p
r
o
v
id
es
r
o
b
u
s
t
an
d
r
eliab
le
f
o
r
ec
asts
o
f
Pak
is
tan
'
s
GDP
g
r
o
wth
r
ate,
o
f
f
er
i
n
g
p
r
ec
is
e
esti
m
ates
f
o
r
th
e
y
ea
r
s
2
0
2
3
,
2
0
2
4
,
2
0
2
5
,
an
d
2
0
2
6
:
3
.
7
6
3
%,
3
.
4
5
%,
4
.
7
2
1
%,
an
d
5
.
3
4
%,
r
esp
ec
tiv
ely
.
T
h
e
im
p
licatio
n
s
o
f
th
ese
f
in
d
in
g
s
ex
ten
d
b
e
y
o
n
d
ac
ad
em
ic
r
esear
ch
,
s
u
g
g
esti
n
g
p
r
ac
tical
ap
p
licatio
n
s
f
o
r
ec
o
n
o
m
ic
m
an
ag
em
en
t
team
s
a
n
d
p
o
licy
m
ak
er
s
.
B
y
lev
er
ag
i
n
g
th
e
k
-
NN
m
o
d
el,
p
o
licy
m
ak
er
s
ca
n
an
ticip
ate
f
u
tu
r
e
ec
o
n
o
m
ic
ac
tiv
ities
m
o
r
e
ac
cu
r
ately
,
en
ab
lin
g
th
em
to
f
o
r
m
u
late
in
f
o
r
m
ed
s
tr
ateg
ies
an
d
p
o
licies
to
m
iti
g
ate
ch
allen
g
es
an
d
p
r
o
m
o
te
s
u
s
tain
ab
le
ec
o
n
o
m
ic
g
r
o
wth
.
Fu
r
th
er
m
o
r
e,
th
e
s
tu
d
y
ca
lls
u
p
o
n
th
e
g
o
v
er
n
m
en
t
o
f
Pak
is
tan
to
im
p
lem
e
n
t
p
r
u
d
en
t
ec
o
n
o
m
ic
p
o
licies
alig
n
ed
with
th
ese
f
o
r
ec
asts
to
en
h
an
ce
ec
o
n
o
m
ic
s
tab
ilit
y
an
d
p
r
o
s
p
er
ity
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
E
va
lu
a
tio
n
o
f m
a
ch
in
e
lea
r
n
i
n
g
a
p
p
r
o
a
ch
in
mo
d
ellin
g
a
n
d
fo
r
ec
a
s
tin
g
r
ea
l g
r
o
s
s
…
(
Mo
iz
Qu
r
esh
i
)
1347
F
UNDING
I
NF
O
R
M
A
T
I
O
N
No
f
u
n
d
in
g
was
s
ec
u
r
ed
f
o
r
t
h
is
s
tu
d
y
.
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
No
co
n
f
lict o
f
in
ter
est is
d
ec
lar
ed
b
y
th
e
au
th
o
r
s
.
DATA
AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
is
m
ad
e
u
p
o
f
Pak
is
tan
’
s
GDP
g
r
o
wth
r
at
e
f
r
o
m
1
9
9
0
to
2
0
2
2
,
av
ailab
le
at
h
ttp
s
:
//d
a
ta
.
w
o
r
ld
b
a
n
k
.
o
r
g
/in
d
ica
to
r
/N
Y.GD
P
.
M
K
TP.
K
D.
Z
G?
lo
ca
tio
n
s
=P
K
.
RE
F
E
R
E
NC
E
S
[
1
]
W
o
r
l
d
B
a
n
k
G
r
o
u
p
,
“
G
D
P
g
r
o
w
t
h
(
a
n
n
u
a
l
%)
-
P
a
k
i
s
t
a
n
.
”
W
o
r
l
d
B
a
n
k
G
r
o
u
p
,
A
c
c
e
ss
e
d
:
S
e
p
.
1
2
,
2
0
2
2
.
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s
:
/
/
d
a
t
a
.
w
o
r
l
d
b
a
n
k
.
o
r
g
/
i
n
d
i
c
a
t
o
r
/
N
Y
.
G
D
P
.
M
K
TP.
K
D
.
ZG
?
l
o
c
a
t
i
o
n
s=P
K
.
[
2
]
S
.
K
h
a
n
,
“
I
mp
a
c
t
o
f
s
o
u
r
c
e
s
o
f
f
i
n
a
n
c
e
o
n
t
h
e
g
r
o
w
t
h
o
f
S
M
Es
:
e
v
i
d
e
n
c
e
f
r
o
m
P
a
k
i
st
a
n
,
”
D
E
C
I
S
I
O
N
,
v
o
l
.
4
2
,
n
o
.
1
,
p
p
.
3
–
1
0
,
M
a
r
.
2
0
1
5
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
0
6
2
2
-
0
1
4
-
0
0
7
1
-
z.
[
3
]
M
.
W
.
A
mi
r
,
A
.
B
i
b
i
,
N
.
A
k
h
t
a
r
,
a
n
d
Z.
R
a
z
a
,
“
M
o
d
e
l
i
n
g
a
n
d
f
o
r
e
c
a
s
t
i
n
g
o
f
g
r
o
ss
d
o
mes
t
i
c
p
r
o
d
u
c
t
p
e
r
c
e
n
t
a
g
e
s
h
a
r
e
o
f
e
d
u
c
a
t
i
o
n
sec
t
o
r
:
a
s
t
a
t
i
s
t
i
c
a
l
st
u
d
y
i
n
P
a
k
i
st
a
n
,
”
T
ra
n
sa
c
t
i
o
n
s
i
n
M
a
t
h
e
m
a
t
i
c
a
l
a
n
d
C
o
m
p
u
t
a
t
i
o
n
a
l
S
c
i
e
n
c
e
s
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
7
5
–
8
4
,
2
0
2
1
.
[
4
]
C
.
S
.
H
.
W
a
n
g
,
R
.
F
a
n
,
a
n
d
Y
.
X
i
e
,
“
M
a
r
k
e
t
s
y
st
e
mi
c
r
i
sk
,
p
r
e
d
i
c
t
a
b
i
l
i
t
y
a
n
d
ma
c
r
o
e
c
o
n
o
m
i
c
s
n
e
w
s,”
F
i
n
a
n
c
e
Re
se
a
rc
h
L
e
t
t
e
rs
,
v
o
l
.
5
6
,
p
.
1
0
4
1
0
2
,
S
e
p
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
f
r
l
.
2
0
2
3
.
1
0
4
1
0
2
.
[
5
]
P
.
P
i
l
s
t
r
ö
m
a
n
d
S
.
P
o
h
l
,
“
F
o
r
e
c
a
st
i
n
g
G
D
P
g
r
o
w
t
h
:
t
h
e
c
a
se
o
f
t
h
e
B
a
l
t
i
c
S
t
a
t
e
s,”
M
S
T
h
e
s
i
s,
J
ö
n
k
ö
p
i
n
g
I
n
t
e
r
n
a
t
i
o
n
a
l
B
u
si
n
e
ss
S
c
h
o
o
l
,
Jö
n
k
ö
p
i
n
g
U
n
i
v
e
r
si
t
y
,
J
ö
n
k
ö
p
i
n
g
,
S
w
e
d
e
n
,
2
0
0
9
.
[
6
]
N
.
I
.
D
o
r
é
,
“
E
c
o
n
o
mi
c
g
r
o
w
t
h
a
n
d
c
o
n
v
e
r
g
e
n
c
e
i
n
t
h
e
v
e
r
y
l
o
n
g
-
r
u
n
:
t
h
e
c
a
s
e
o
f
e
m
e
r
g
i
n
g
e
c
o
n
o
m
i
e
s
w
i
t
h
a
f
o
c
u
s
o
n
B
r
a
z
i
l
,
”
U
n
i
v
e
r
si
t
y
o
f
P
o
r
t
o
,
2
0
2
2
.
[
7
]
G
.
K
o
o
p
,
S
.
M
c
I
n
t
y
r
e
,
J.
M
i
t
c
h
e
l
l
,
a
n
d
A
.
P
o
o
n
,
“
R
e
c
o
n
c
i
l
e
d
e
st
i
ma
t
e
s
a
n
d
n
o
w
c
a
st
s
o
f
r
e
g
i
o
n
a
l
o
u
t
p
u
t
i
n
t
h
e
U
K
,
”
N
a
t
i
o
n
a
l
I
n
st
i
t
u
t
e
E
c
o
n
o
m
i
c
Re
v
i
e
w
,
v
o
l
.
2
5
3
,
p
p
.
R
4
4
--
R
5
9
,
A
u
g
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
7
/
n
i
e
.
2
0
2
0
.
2
9
.
[
8
]
S
.
R
.
B
a
b
u
r
a
a
n
d
Y
.
M
u
s
t
a
p
h
a
,
“
S
c
r
e
e
n
i
n
g
f
o
r
d
e
v
e
l
o
p
me
n
t
o
f
h
o
s
t
p
l
a
n
t
r
e
si
s
t
a
n
c
e
t
o
i
n
f
e
s
t
a
t
i
o
n
b
y
a
p
h
i
d
(
A
p
h
i
s
c
r
a
c
c
i
v
o
r
a
K
o
c
h
)
i
n
c
o
w
p
e
a
(
V
i
g
n
a
u
n
g
u
i
c
u
l
a
t
a
[
L]
W
a
l
p
)
,
”
B
a
y
e
r
o
J
o
u
r
n
a
l
o
f
P
u
re
a
n
d
A
p
p
l
i
e
d
S
c
i
e
n
c
e
s
,
v
o
l
.
5
,
n
o
.
1
,
p
p
.
4
4
–
4
7
,
2
0
1
2
.
[
9
]
K
.
D
r
e
c
h
sel
a
n
d
R
.
S
c
h
e
u
f
e
l
e
,
“
T
h
e
p
e
r
f
o
r
ma
n
c
e
o
f
s
h
o
r
t
-
t
e
r
m fo
r
e
c
a
s
t
s
o
f
t
h
e
G
e
r
ma
n
e
c
o
n
o
m
y
b
e
f
o
r
e
a
n
d
d
u
r
i
n
g
t
h
e
2
0
0
8
/
2
0
0
9
r
e
c
e
ss
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
F
o
re
c
a
s
t
i
n
g
,
v
o
l
.
2
8
,
n
o
.
2
,
p
p
.
4
2
8
–
4
4
5
,
A
p
r
.
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
f
o
r
e
c
a
s
t
.
2
0
1
1
.
0
4
.
0
0
3
.
[
1
0
]
A
.
Ti
mm
e
r
ma
n
n
,
“
C
h
a
p
t
e
r
4
f
o
r
e
c
a
st
c
o
mb
i
n
a
t
i
o
n
s
,
”
H
a
n
d
b
o
o
k
o
f
e
c
o
n
o
m
i
c
f
o
r
e
c
a
st
i
n
g
,
v
o
l
.
1
,
p
p
.
1
3
5
–
1
9
6
,
2
0
0
6
,
d
o
i
:
1
0
.
1
0
1
6
/
S
1
5
7
4
-
0
7
0
6
(
0
5
)
0
1
0
0
4
-
9.
[
1
1
]
D
.
S
h
a
h
,
W
.
C
a
mp
b
e
l
l
,
a
n
d
F
.
H
.
Zu
l
k
e
r
n
i
n
e
,
“
A
c
o
m
p
a
r
a
t
i
v
e
st
u
d
y
o
f
L
S
TM
a
n
d
D
N
N
f
o
r
st
o
c
k
mark
e
t
f
o
r
e
c
a
st
i
n
g
,
”
i
n
2
0
1
8
I
EEE
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
B
i
g
D
a
t
a
(
B
i
g
D
a
t
a
)
,
D
e
c
.
2
0
1
8
,
p
p
.
4
1
4
8
–
4
1
5
5
,
d
o
i
:
1
0
.
1
1
0
9
/
B
i
g
D
a
t
a
.
2
0
1
8
.
8
6
2
2
4
6
2
.
[
1
2
]
M
.
A
.
K
h
a
n
e
t
a
l
.
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
f
o
r
s
u
s
t
a
i
n
a
b
l
e
b
u
si
n
e
ss
ma
n
a
g
e
me
n
t
b
a
se
d
o
n
mac
r
o
-
e
c
o
n
o
m
i
c
d
a
t
a
:
su
p
e
r
v
i
se
d
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s a
p
p
r
o
a
c
h
,
”
S
u
st
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
1
4
,
n
o
.
1
6
,
p
.
9
9
6
4
,
A
u
g
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
su
1
4
1
6
9
9
6
4
.
[
1
3
]
M
.
V
a
s
u
d
e
v
a
n
a
n
d
O
t
h
e
r
s
,
“
C
r
e
d
i
t
r
i
sk
m
o
d
e
l
i
n
g
:
a
c
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
si
s
o
f
a
r
t
i
f
i
c
i
a
l
a
n
d
d
e
e
p
n
e
u
r
a
l
n
e
t
w
o
r
k
s,”
T
h
o
mp
so
n
R
i
v
e
r
s U
n
i
v
e
r
s
i
t
y
,
2
0
2
0
.
[
1
4
]
H
.
B
o
u
sq
a
o
u
i
,
I
.
S
l
i
ma
n
i
,
a
n
d
S
.
A
c
h
c
h
a
b
,
“
C
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
si
s
o
f
s
h
o
r
t
-
t
e
r
m
d
e
ma
n
d
p
r
e
d
i
c
t
i
n
g
m
o
d
e
l
s
u
s
i
n
g
A
R
I
M
A
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
t
ri
c
a
l
a
n
d
C
o
m
p
u
t
e
r
En
g
i
n
e
e
ri
n
g
(
I
J
EC
E)
,
v
o
l
.
1
1
,
n
o
.
4
,
p
.
3
3
1
9
,
A
u
g
.
2
0
2
1
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
e
c
e
.
v
1
1
i
4
.
p
p
3
3
1
9
-
3
3
2
8
.
[
1
5
]
Y
.
F
.
S
a
f
r
i
,
R
.
A
r
i
f
u
d
i
n
,
a
n
d
M
.
A
.
M
u
s
l
i
m
,
“
K
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
a
n
d
n
a
i
v
e
B
a
y
e
s
c
l
a
ss
i
f
i
e
r
a
l
g
o
r
i
t
h
m
i
n
d
e
t
e
r
mi
n
i
n
g
t
h
e
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
h
e
a
l
t
h
y
c
a
r
d
I
n
d
o
n
e
si
a
g
i
v
i
n
g
t
o
t
h
e
p
o
o
r
,
”
S
c
i
e
n
t
i
f
i
c
J
o
u
r
n
a
l
o
f
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
5
,
n
o
.
1
,
p
p
.
9
-
1
8
,
2
0
1
8
.
[
1
6
]
B
.
P
r
i
a
m
b
o
d
o
e
t
a
l
.
,
“
P
r
e
d
i
c
t
i
n
g
G
D
P
o
f
I
n
d
o
n
e
si
a
u
si
n
g
k
-
n
e
a
r
e
st
n
e
i
g
h
b
o
u
r
r
e
g
r
e
ss
i
o
n
,
”
J
o
u
r
n
a
l
o
f
Ph
y
si
c
s:
C
o
n
f
e
r
e
n
c
e
S
e
ri
e
s
,
v
o
l
.
1
3
3
9
,
n
o
.
1
,
p
.
1
2
0
4
0
,
D
e
c
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
2
-
6
5
9
6
/
1
3
3
9
/
1
/
0
1
2
0
4
0
.
[
1
7
]
M
.
Q
u
r
e
s
h
i
,
N
.
A
h
m
a
d
,
S
.
U
l
l
a
h
,
a
n
d
A
.
R
.
u
l
M
u
s
t
a
f
a
,
“
F
o
r
e
c
a
s
t
i
n
g
r
e
a
l
e
x
c
h
a
n
g
e
r
a
t
e
(
R
EE
R
)
u
si
n
g
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
a
n
d
t
i
m
e
ser
i
e
s
mo
d
e
l
s,
”
H
e
l
i
y
o
n
,
v
o
l
.
9
,
n
o
.
5
,
2
0
2
3
.
[
1
8
]
D
.
G
u
é
g
a
n
a
n
d
P
.
R
a
k
o
t
o
mar
o
l
a
h
y
,
“
Th
e
mu
l
t
i
v
a
r
i
a
t
e
k
-
n
e
a
r
e
st
n
e
i
g
h
b
o
r
mo
d
e
l
f
o
r
d
e
p
e
n
d
e
n
t
v
a
r
i
a
b
l
e
s
:
o
n
e
-
si
d
e
d
e
s
t
i
m
a
t
i
o
n
a
n
d
f
o
r
e
c
a
st
i
n
g
,
”
D
o
c
u
me
n
t
s
d
e
t
r
a
v
a
i
l
d
u
C
e
n
t
r
e
d
’
E
c
o
n
o
mi
e
d
e
l
a
S
o
r
b
o
n
n
e
,
2
0
0
9
.
[
1
9
]
F
.
P
e
t
r
o
p
o
u
l
o
s
e
t
a
l
.
,
“
F
o
r
e
c
a
st
i
n
g
:
t
h
e
o
r
y
a
n
d
p
r
a
c
t
i
c
e
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
F
o
r
e
c
a
st
i
n
g
,
v
o
l
.
3
8
,
n
o
.
3
,
p
p
.
7
0
5
–
8
7
1
,
J
u
l
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
f
o
r
e
c
a
s
t
.
2
0
2
1
.
1
1
.
0
0
1
.
[
2
0
]
M
.
Q
u
r
e
s
h
i
,
M
.
D
a
n
i
y
a
l
,
a
n
d
K
.
Ta
w
i
a
h
,
“
C
o
m
p
a
r
a
t
i
v
e
e
v
a
l
u
a
t
i
o
n
o
f
t
h
e
mu
l
t
i
l
a
y
e
r
p
e
r
c
e
p
t
r
o
n
a
p
p
r
o
a
c
h
w
i
t
h
c
o
n
v
e
n
t
i
o
n
a
l
A
R
I
M
A
i
n
m
o
d
e
l
i
n
g
a
n
d
p
r
e
d
i
c
t
i
o
n
o
f
C
O
V
I
D
-
1
9
d
a
i
l
y
d
e
a
t
h
c
a
s
e
s,”
J
o
u
rn
a
l
o
f
H
e
a
l
t
h
c
a
r
e
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
2
0
2
2
,
p
p
.
1
–
7
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
2
/
4
8
6
4
9
2
0
.
[
2
1
]
M
.
Ja
h
n
,
“
A
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
r
e
g
r
e
ss
i
o
n
m
o
d
e
l
s
:
p
r
e
d
i
c
t
i
n
g
G
D
P
g
r
o
w
t
h
,
”
H
W
WI
R
e
se
a
rc
h
Pa
p
e
r
,
N
o
.
1
8
5
,
H
a
mb
u
r
g
i
sc
h
e
s
W
e
l
t
W
i
r
t
sc
h
a
f
t
sI
n
st
i
t
u
t
(
H
W
W
I
)
,
H
a
m
b
u
r
g
,
2
0
1
8
.
[
2
2
]
M
.
K
.
A
h
u
j
a
,
A
.
G
o
t
l
i
e
b
,
a
n
d
H
.
S
p
i
e
k
e
r
,
“
T
e
st
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
m
o
d
e
l
s:
a
f
i
r
st
c
o
m
p
a
r
a
t
i
v
e
st
u
d
y
o
f
m
u
l
t
i
p
l
e
t
e
s
t
i
n
g
t
e
c
h
n
i
q
u
e
s
,
”
i
n
2
0
2
2
I
E
EE
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
S
o
f
t
w
a
r
e
T
e
s
t
i
n
g
,
V
e
ri
f
i
c
a
t
i
o
n
a
n
d
V
a
l
i
d
a
t
i
o
n
W
o
rks
h
o
p
s
(
I
C
S
T
W)
,
A
p
r
.
2
0
2
2
,
p
p
.
1
3
0
–
1
3
7
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
TW5
5
3
9
5
.
2
0
2
2
.
0
0
0
3
5
.
[
2
3
]
C.
-
Y
.
H
u
n
g
,
C
.
-
C
.
W
a
n
g
,
S
.
-
W
.
L
i
n
,
a
n
d
B
.
C
.
Ji
a
n
g
,
“
A
n
e
m
p
i
r
i
c
a
l
c
o
mp
a
r
i
so
n
o
f
t
h
e
s
a
l
e
s
f
o
r
e
c
a
st
i
n
g
p
e
r
f
o
r
man
c
e
f
o
r
p
l
a
s
t
i
c
t
r
a
y
ma
n
u
f
a
c
t
u
r
i
n
g
u
si
n
g
m
i
ssi
n
g
d
a
t
a
,
”
S
u
s
t
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
1
4
,
n
o
.
4
,
p
.
2
3
8
2
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
su
1
4
0
4
2
3
8
2
.
[
2
4
]
M
.
Q
u
r
e
sh
i
a
n
d
N
.
A
h
m
a
d
,
“
F
o
r
e
c
a
s
t
i
n
g
c
r
y
p
t
o
c
u
r
r
e
n
c
i
e
s
u
s
i
n
g
t
h
e
c
l
a
ss
i
c
a
l
t
i
me
s
e
r
i
e
s
a
p
p
r
o
a
c
h
,
”
K
A
S
BI
T
B
u
si
n
e
ss
J
o
u
rn
a
l
,
v
o
l
.
1
5
,
n
o
.
2
,
2
0
2
2
.
[
2
5
]
A
.
R
i
c
h
a
r
d
s
o
n
,
T
.
M
u
l
d
e
r
,
a
n
d
T.
l
V
e
h
b
i
,
“
N
o
w
c
a
s
t
i
n
g
N
e
w
Ze
a
l
a
n
d
G
D
P
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms,
”
S
S
R
N
El
e
c
t
ro
n
i
c
J
o
u
rn
a
l
,
2
0
1
8
,
d
o
i
:
1
0
.
2
1
3
9
/
ssr
n
.
3
2
5
6
5
7
8
.
[
2
6
]
I
.
S
v
e
t
u
n
k
o
v
,
H
.
C
h
e
n
,
a
n
d
J.
E.
B
o
y
l
a
n
,
“
A
n
e
w
t
a
x
o
n
o
m
y
f
o
r
v
e
c
t
o
r
e
x
p
o
n
e
n
t
i
a
l
sm
o
o
t
h
i
n
g
a
n
d
i
t
s
a
p
p
l
i
c
a
t
i
o
n
t
o
sea
s
o
n
a
l
t
i
m
e
seri
e
s
,
”
Eu
r
o
p
e
a
n
J
o
u
r
n
a
l
o
f
O
p
e
r
a
t
i
o
n
a
l
Re
se
a
rc
h
,
v
o
l
.
3
0
4
,
n
o
.
3
,
p
p
.
9
6
4
–
9
8
0
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
j
o
r
.
2
0
2
2
.
0
4
.
0
4
0
.
[
2
7
]
M
.
U
d
e
n
i
o
,
E
.
V
a
t
a
mi
d
o
u
,
a
n
d
J
.
C
.
F
r
a
n
s
o
o
,
“
E
x
p
o
n
e
n
t
i
a
l
s
mo
o
t
h
i
n
g
f
o
r
e
c
a
st
s
:
t
a
m
i
n
g
t
h
e
b
u
l
l
w
h
i
p
e
f
f
e
c
t
w
h
e
n
d
e
m
a
n
d
i
s
sea
s
o
n
a
l
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Pro
d
u
c
t
i
o
n
Re
se
a
r
c
h
,
v
o
l
.
6
1
,
n
o
.
6
,
p
p
.
1
7
9
6
–
1
8
1
3
,
M
a
r
.
2
0
2
3
,
d
o
i
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
3
3
9
-
1
3
4
9
1348
1
0
.
1
0
8
0
/
0
0
2
0
7
5
4
3
.
2
0
2
2
.
2
0
4
8
1
1
4
.
[
2
8
]
C
.
S
.
B
o
j
e
r
,
“
U
n
d
e
r
s
t
a
n
d
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
-
b
a
s
e
d
f
o
r
e
c
a
s
t
i
n
g
m
e
t
h
o
d
s
:
a
d
e
c
o
m
p
o
s
i
t
i
o
n
f
r
a
m
e
w
o
r
k
a
n
d
r
e
s
e
a
r
c
h
o
p
p
o
r
t
u
n
i
t
i
e
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
F
o
r
e
c
a
s
t
i
n
g
,
v
o
l
.
3
8
,
n
o
.
4
,
p
p
.
1
5
5
5
–
1
5
6
1
,
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
f
o
r
e
c
a
s
t
.
2
0
2
1
.
1
1
.
0
0
3
.
[
2
9
]
I
.
H
.
C
h
u
n
g
,
D
.
W
.
W
i
l
l
i
a
ms
,
a
n
d
M
.
R
.
D
o
,
“
F
o
r
b
e
t
t
e
r
o
r
w
o
r
se
?
R
e
v
e
n
u
e
f
o
r
e
c
a
s
t
i
n
g
w
i
t
h
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s,”
Pu
b
l
i
c
Pe
r
f
o
rm
a
n
c
e
& M
a
n
a
g
e
m
e
n
t
Re
v
i
e
w
,
v
o
l
.
4
5
,
n
o
.
5
,
p
p
.
1
1
3
3
–
1
1
5
4
,
S
e
p
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
3
0
9
5
7
6
.
2
0
2
2
.
2
0
7
3
5
5
1
.
[
3
0
]
S
.
U
d
d
i
n
,
I
.
H
a
q
u
e
,
H
.
L
u
,
M
.
A
.
M
o
n
i
,
a
n
d
E.
G
i
d
e
,
“
C
o
m
p
a
r
a
t
i
v
e
p
e
r
f
o
r
m
a
n
c
e
a
n
a
l
y
si
s
o
f
K
-
n
e
a
r
e
st
n
e
i
g
h
b
o
u
r
(
K
N
N
)
a
l
g
o
r
i
t
h
m
a
n
d
i
t
s
d
i
f
f
e
r
e
n
t
v
a
r
i
a
n
t
s
f
o
r
d
i
sea
s
e
p
r
e
d
i
c
t
i
o
n
,
”
S
c
i
e
n
t
i
f
i
c
Re
p
o
r
t
s
,
v
o
l
.
1
2
,
n
o
.
1
,
p
.
6
2
5
6
,
A
p
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
3
8
/
s
4
1
5
9
8
-
0
2
2
-
1
0
3
5
8
-
x.
[
3
1
]
M
.
D
a
n
i
y
a
l
,
K
.
Ta
w
i
a
h
,
S
.
M
u
h
a
m
ma
d
u
l
l
a
h
,
a
n
d
K
.
O
p
o
k
u
-
A
m
e
y
a
w
,
“
C
o
mp
a
r
i
so
n
o
f
c
o
n
v
e
n
t
i
o
n
a
l
m
o
d
e
l
i
n
g
t
e
c
h
n
i
q
u
e
s
w
i
t
h
t
h
e
n
e
u
r
a
l
n
e
t
w
o
r
k
a
u
t
o
r
e
g
r
e
ssi
v
e
mo
d
e
l
(
N
N
A
R
)
:
a
p
p
l
i
c
a
t
i
o
n
t
o
C
O
V
I
D
-
1
9
d
a
t
a
,
”
J
o
u
rn
a
l
o
f
H
e
a
l
t
h
c
a
re
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
2
0
2
2
,
p
p
.
1
–
9
,
Ju
n
.
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
2
/
4
8
0
2
7
4
3
.
[
3
2
]
M
.
Q
u
r
e
s
h
i
,
A
.
K
h
a
n
,
M
.
D
a
n
i
y
a
l
,
K
.
Ta
w
i
a
h
,
a
n
d
Z
.
M
e
h
mo
o
d
,
“
A
c
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
s
i
s
o
f
t
r
a
d
i
t
i
o
n
a
l
S
A
R
I
M
A
a
n
d
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
mo
d
e
l
s
f
o
r
C
P
I
d
a
t
a
m
o
d
e
l
l
i
n
g
i
n
P
a
k
i
s
t
a
n
,
”
A
p
p
l
i
e
d
C
o
m
p
u
t
a
t
i
o
n
a
l
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
S
o
f
t
C
o
m
p
u
t
i
n
g
,
v
o
l
.
2
0
2
3
,
p
p
.
1
–
1
0
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
3
/
3
2
3
6
6
1
7
.
[
3
3
]
H
.
I
f
t
i
k
h
a
r
,
M
.
D
a
n
i
y
a
l
,
M
.
Q
u
r
e
sh
i
,
K
.
Ta
w
i
a
h
,
R
.
K
.
A
n
sa
h
,
a
n
d
J.
K
.
A
f
r
i
y
i
e
,
“
A
h
y
b
r
i
d
f
o
r
e
c
a
st
i
n
g
t
e
c
h
n
i
q
u
e
f
o
r
i
n
f
e
c
t
i
o
n
a
n
d
d
e
a
t
h
f
r
o
m
t
h
e
mp
o
x
v
i
r
u
s,”
D
I
G
I
T
AL H
EALT
H
,
v
o
l
.
9
,
Ja
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
7
7
/
2
0
5
5
2
0
7
6
2
3
1
2
0
4
7
4
8
.
[
3
4
]
K
.
T
a
w
i
a
h
,
M
.
D
a
n
i
y
a
l
,
a
n
d
M
.
Q
u
r
e
sh
i
,
“
P
a
k
i
st
a
n
C
O
2
e
m
i
ssi
o
n
mo
d
e
l
l
i
n
g
a
n
d
f
o
r
e
c
a
st
i
n
g
:
a
l
i
n
e
a
r
a
n
d
n
o
n
l
i
n
e
a
r
t
i
m
e
s
e
r
i
e
s
a
p
p
r
o
a
c
h
,
”
J
o
u
rn
a
l
o
f
E
n
v
i
r
o
n
m
e
n
t
a
l
a
n
d
Pu
b
l
i
c
H
e
a
l
t
h
,
v
o
l
.
2
0
2
3
,
p
p
.
1
–
1
5
,
J
a
n
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
5
/
2
0
2
3
/
5
9
0
3
3
6
2
.
[
3
5
]
M
.
Q
u
r
e
s
h
i
e
t
a
l
.
,
“
M
o
d
e
l
i
n
g
a
n
d
f
o
r
e
c
a
s
t
i
n
g
mo
n
k
e
y
p
o
x
c
a
ses
u
si
n
g
st
o
c
h
a
s
t
i
c
mo
d
e
l
s,
”
J
o
u
r
n
a
l
o
f
C
l
i
n
i
c
a
l
Me
d
i
c
i
n
e
,
v
o
l
.
1
1
,
n
o
.
2
1
,
p
.
6
5
5
5
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
j
c
m1
1
2
1
6
5
5
5
.
[
3
6
]
H
.
I
f
t
i
k
h
a
r
,
M
.
Q
u
r
e
s
h
i
,
J.
Zy
w
i
o
ł
e
k
,
J.
L.
Ló
p
e
z
-
G
o
n
z
a
l
e
s
,
a
n
d
O
.
A
l
b
a
l
a
w
i
,
“
S
h
o
r
t
-
t
e
r
m
P
M
2
.
5
f
o
r
e
c
a
st
i
n
g
u
si
n
g
a
u
n
i
q
u
e
e
n
s
e
mb
l
e
t
e
c
h
n
i
q
u
e
f
o
r
p
r
o
a
c
t
i
v
e
e
n
v
i
r
o
n
m
e
n
t
a
l
ma
n
a
g
e
m
e
n
t
i
n
i
t
i
a
t
i
v
e
s,
”
F
ro
n
t
i
e
rs
i
n
En
v
i
r
o
n
m
e
n
t
a
l
S
c
i
e
n
c
e
,
v
o
l
.
1
2
,
S
e
p
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
8
9
/
f
e
n
v
s.
2
0
2
4
.
1
4
4
2
6
4
4
.
[
3
7
]
M
.
Q
u
r
e
s
h
i
,
H
.
I
f
t
i
k
h
a
r
,
P
.
C
.
R
o
d
r
i
g
u
e
s
,
M
.
Z.
R
e
h
ma
n
,
a
n
d
S
.
A
.
A
.
S
a
l
a
r
,
“
S
t
a
t
i
st
i
c
a
l
m
o
d
e
l
i
n
g
t
o
i
m
p
r
o
v
e
t
i
me
s
e
r
i
e
s
f
o
r
e
c
a
st
i
n
g
u
si
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
t
i
m
e
seri
e
s,
a
n
d
h
y
b
r
i
d
mo
d
e
l
s
:
a
c
a
se
st
u
d
y
o
f
b
i
t
c
o
i
n
p
r
i
c
e
f
o
r
e
c
a
st
i
n
g
,
”
M
a
t
h
e
m
a
t
i
c
s
,
v
o
l
.
1
2
,
n
o
.
2
3
,
p
.
3
6
6
6
,
N
o
v
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
ma
t
h
1
2
2
3
3
6
6
6
.
[
3
8
]
Y
.
K
i
n
z
a
,
Q
.
M
o
i
z
,
D
.
M
u
h
a
mm
a
d
,
a
n
d
I
.
M
u
h
a
mm
a
d
,
“
C
o
m
p
a
r
a
t
i
v
e
st
u
d
y
o
f
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
(
M
L)
a
n
d
c
o
n
v
e
n
t
i
o
n
a
l
t
i
m
e
seri
e
s
m
e
t
h
o
d
o
l
o
g
i
e
s
i
n
m
o
d
e
l
l
i
n
g
t
h
e
e
x
p
o
r
t
s
t
r
a
d
e
o
f
P
a
k
i
s
t
a
n
,
”
I
N
D
U
S
J
O
U
RN
AL
O
F
S
O
C
I
AL
S
C
I
EN
C
E
S
Учр
е
ди
т
е
л
и
:
A
l
i
I
n
st
i
t
u
t
e
o
f
Re
se
a
r
c
h
&
S
k
i
l
l
s De
v
e
l
o
p
m
e
n
t
,
v
o
l
.
2
,
n
o
.
2
,
p
p
.
3
4
9
–
3
6
7
,
2
0
2
4
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Mo
iz
Q
u
r
e
shi
is
a
n
M
.
P
h
il
.
s
c
h
o
lar
in
s
tatist
ics
fro
m
Q
u
a
id
-
e
-
A
z
a
m
Un
iv
e
rsity
Isla
m
a
b
a
d
,
a
n
d
l
e
c
tu
re
r
in
s
tat
isti
c
s
a
t
G
o
v
e
rn
m
e
n
t
Bo
y
s
De
g
re
e
Co
ll
e
g
e
Tan
d
o
Ja
m
Hy
d
e
ra
b
a
d
S
in
d
h
P
a
k
istan
.
Be
fo
r
e
jo
in
i
n
g
G
DC,
h
e
wo
rk
e
d
a
s
a
l
e
c
tu
re
r
a
t
S
h
a
h
e
e
d
Be
n
a
z
ir
Bh
u
tt
o
Un
i
v
e
rsity
Na
wa
b
sh
a
h
.
M
o
iz
Qu
re
sh
i
re
se
a
rc
h
in
tere
sts
a
re
s
tatisti
c
a
l
p
ro
c
e
ss
c
o
n
tro
l,
ti
m
e
se
ries
a
n
a
ly
sis,
m
a
c
h
in
e
lea
rn
i
n
g
,
a
rti
ficia
l
i
n
telli
g
e
n
c
e
,
re
g
re
ss
io
n
a
n
a
ly
sis
,
b
io
sta
ti
stics
a
n
d
e
p
i
d
e
m
io
lo
g
y
,
b
u
sin
e
ss
sta
ti
stics
,
fin
a
n
c
ial
a
n
a
ly
sis,
a
n
d
e
c
o
n
o
m
e
tri
c
s
.
He
a
lso
se
rv
e
s
a
s
re
v
iew
e
r
in
m
a
n
y
j
o
u
r
n
a
ls
(
p
u
b
l
ish
e
rs)
su
c
h
a
s,
El
se
v
ier,
I
EE
E,
P
e
e
rJ,
S
p
rin
g
e
r,
S
AG
E
a
n
d
o
t
h
e
rs
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
o
iz@
sta
t.
q
a
u
.
e
d
u
.
p
k
.
Mu
h
a
m
m
a
d
Is
m
a
il
is
p
u
rs
u
in
g
a
n
M
.
P
h
il
.
i
n
s
tatisti
c
s
a
t
Qu
a
id
-
i
-
Az
a
m
Un
iv
e
rsity
,
Isla
m
a
b
a
d
.
He
wo
r
k
e
d
a
s
a
l
e
c
tu
re
r
a
t
th
e
G
o
v
e
rn
m
e
n
t
P
o
st
g
ra
d
u
a
te
Co
ll
e
g
e
No
ws
h
e
ra
,
KPK,
P
a
k
istan
.
His
re
se
a
rc
h
fo
c
u
se
s
o
n
wa
ter
m
a
n
a
g
e
m
e
n
t,
m
e
teo
ro
lo
g
y
,
sp
a
ti
a
l
sta
ti
stics
,
g
e
o
-
sp
a
ti
a
l
e
n
v
ir
o
n
m
e
n
tal
h
a
z
a
rd
s
d
a
ta
m
o
d
e
li
n
g
a
n
d
th
e
d
e
p
lo
y
m
e
n
t
o
f
m
o
d
e
r
n
sta
ti
stica
l
a
p
p
ro
a
c
h
e
s
f
o
r
n
a
t
u
ra
l
p
ro
c
e
ss
m
o
n
it
o
ri
n
g
.
He
h
a
s
a
r
ich
e
x
p
e
rien
c
e
i
n
n
a
tu
ra
l
p
ro
c
e
ss
m
o
n
it
o
ri
n
g
,
c
li
m
a
te
c
h
a
n
g
e
a
n
d
s
p
a
ti
a
l
a
n
a
ly
sis.
He
h
a
s
a
se
lf
-
su
fficie
n
t,
e
x
c
e
ll
e
n
t
c
o
m
m
u
n
ica
ti
o
n
,
writi
n
g
a
n
d
d
a
ta
in
terp
re
tatio
n
sk
il
ls.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
.
ism
a
il
@s
tat.
q
a
u
.
e
d
u
.
p
k
.
Na
wa
z
Ahm
a
d
is
a
n
a
ss
o
c
iat
e
p
ro
fe
ss
o
r
a
n
d
c
h
a
irma
n
o
f
t
h
e
De
p
a
rtme
n
t
o
f
Bu
sin
e
ss
Ad
m
in
istrati
o
n
;
Co
n
v
e
n
e
r
o
f
t
h
e
Re
se
a
rc
h
S
o
c
iety
a
t
S
h
a
h
e
e
d
Be
n
a
z
ir
B
h
u
t
to
Un
iv
e
rsity
,
P
a
k
istan
;
A
d
v
is
o
ry
Bo
a
rd
M
e
m
b
e
r
o
f
Et
h
ica
l
F
u
n
d
in
g
Co
.
,
USA;
p
ri
n
c
ip
a
l
c
o
n
su
lt
a
n
t
a
t
Re
se
a
rc
h
Train
in
g
S
o
lu
t
io
n
s
(
RTS
),
Ka
ra
c
h
i;
b
u
si
n
e
ss
c
o
n
su
lt
a
n
t
,
Na
ti
o
n
a
l
Bu
sin
e
ss
De
v
e
lo
p
m
e
n
t
o
f
P
a
k
istan
;
a
n
d
c
o
n
s
u
lt
a
n
t
,
As
ian
De
v
e
lo
p
m
e
n
t
Ba
n
k
.
Als
o
,
h
e
is
a
n
Ed
it
o
rial
Bo
a
rd
M
e
m
b
e
r
o
f
l
e
a
d
i
n
g
n
a
ti
o
n
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
jo
u
r
n
a
ls.
He
h
a
s
a
n
e
x
c
e
ll
e
n
t
in
d
u
stry
l
iaiso
n
a
n
d
is
i
n
v
o
lv
e
d
i
n
d
iffere
n
t
train
i
n
g
a
c
ti
v
it
ies
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
a
wa
z
a
h
m
a
d
1
9
7
6
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.