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W
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[
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ec
o
m
p
o
s
ed
to
o
b
tain
s
m
o
o
th
s
er
ie
s
an
d
m
u
l
tip
le
d
etail
s
er
ies.
N
ex
t,
f
o
r
ec
asti
n
g
m
e
th
o
d
,
s
u
c
h
as
AR
I
M
A
i
s
co
m
m
o
n
l
y
u
s
ed
to
e
x
te
n
d
t
h
e
s
u
b
s
er
ie
s
.
L
ast
l
y
,
th
e
m
u
ltip
le
s
u
b
s
er
ies
is
r
ec
o
n
s
tr
u
cted
to
th
e
o
r
ig
in
al
ti
m
e
s
er
ies
[
9
]
.
I
n
DW
T
,
th
is
is
d
o
n
e
b
y
o
b
tain
i
n
g
t
h
e
s
u
m
m
atio
n
o
f
ea
ch
o
f
th
e
s
u
b
s
er
ies.
Fo
r
ec
asti
n
g
u
s
in
g
MR
A
is
ca
p
ab
le
o
f
o
b
tai
n
a
m
o
r
e
ac
cu
r
ate
f
o
r
ec
asti
n
g
co
m
p
ar
ed
to
d
ir
ec
tly
f
o
r
ec
asti
n
g
th
e
o
r
ig
in
a
l
s
er
ies.
T
h
is
is
b
ec
au
s
e
th
e
v
ar
ia
n
ce
o
f
th
e
s
u
b
s
er
ie
s
is
m
o
r
e
s
tab
le
a
n
d
th
e
y
t
y
p
icall
y
h
av
e
n
o
o
u
tlier
[
1
0
]
.
E
m
p
ir
ical
Mo
d
e
Dec
o
m
p
o
s
iti
o
n
(
E
MD
)
is
a
n
o
th
er
s
ig
n
al
d
ec
o
m
p
o
s
i
tio
n
m
et
h
o
d
th
at
is
f
r
eq
u
en
tl
y
ex
p
lo
r
ed
in
f
o
r
ec
asti
n
g
.
Sev
er
al
s
t
u
d
ies
i
n
w
i
n
d
s
p
ee
d
f
o
r
ec
asti
n
g
an
d
an
n
u
al
p
r
ec
ip
itatio
n
f
o
r
ec
asti
n
g
[
1
2
]
-
[
1
4
]
h
av
e
s
h
o
w
n
th
a
t
f
o
r
ec
ast
in
g
t
h
e
I
M
Fs
o
f
E
M
D
r
es
u
lts
in
m
o
r
e
ac
cu
r
ate
f
o
r
ec
ast
in
g
co
m
p
ar
ed
to
d
ir
ec
t
f
o
r
ec
asti
n
g
.
E
MD
h
a
v
e
b
ee
n
s
h
o
w
n
to
b
e
ab
le
to
i
n
cr
ea
s
e
f
o
r
ec
ast
ac
c
u
r
ac
y
o
f
R
ele
v
a
n
ce
Vec
to
r
Ma
ch
in
e
(
R
VM
)
[
1
4
]
.
J
er
o
m
e
Gi
lles
[
1
5
]
p
r
o
p
o
s
ed
e
m
p
ir
ical
w
a
v
elet
tr
an
s
f
o
r
m
(
E
W
T
)
th
at
id
en
ti
f
ies
th
e
m
an
y
u
n
a
lik
e
in
tr
i
n
s
ic
m
o
d
es
o
f
a
ti
m
e
s
e
r
ies
an
d
e
x
tr
ac
ts
th
e
m
.
C
o
m
p
ar
ed
to
E
MD
,
E
W
T
g
iv
es
a
m
o
r
e
co
n
s
is
te
n
t
d
ec
o
m
p
o
s
itio
n
[
1
5
]
.
Sin
ce
E
W
T
is
co
n
ce
p
tu
all
y
s
i
m
ilar
to
DW
T
,
it
w
a
s
f
o
u
n
d
t
h
at
u
s
i
n
g
E
W
T
in
t
i
m
e
s
er
ie
s
p
r
ed
ictio
n
in
cr
ea
s
es t
h
e
ac
cu
r
ac
y
.
E
W
T
h
as b
ee
n
u
s
ed
in
w
i
n
d
s
p
ee
d
f
o
r
ec
asti
n
g
alo
n
g
w
i
t
h
Gau
s
s
ia
n
P
r
o
ce
s
s
R
eg
r
es
s
io
n
(
GP
R
)
,
an
d
it
w
a
s
f
o
u
n
d
o
u
t
t
h
at
t
h
e
m
o
d
els
t
h
at
u
tili
ze
d
E
W
T
h
av
e
h
i
g
h
er
ac
cu
r
ac
y
co
m
p
ar
ed
to
th
e
m
o
d
els t
h
at
d
o
es i
m
p
le
m
e
n
t E
W
T
[
7
]
,
[
1
6
]
.
I
n
b
o
th
s
tu
d
ies,
E
W
T
w
as
u
s
ed
to
d
en
o
is
e
th
e
in
p
u
t d
ata.
Sev
er
al
s
tu
d
ie
s
h
a
v
e
e
x
p
lo
r
ed
o
n
th
e
u
s
e
o
f
cl
u
s
ter
i
n
g
a
n
al
y
s
i
s
to
f
u
r
t
h
er
i
m
p
r
o
v
e
th
e
f
o
r
ec
ast
ac
cu
r
ac
y
[
1
7
]
.
I
n
[
1
8
]
,
I
MF
f
r
o
m
E
MD
ar
e
clu
s
ter
ed
u
s
in
g
k
-
m
ea
n
s
cl
u
s
ter
i
n
g
o
n
t
h
e
in
s
ta
n
tan
eo
u
s
f
r
eq
u
e
n
c
y
o
f
th
e
I
MF
s
.
I
n
[
1
9
]
,
[
2
0
]
,
P
er
m
u
tatio
n
D
is
tr
ib
u
tio
n
C
lu
s
ter
i
n
g
(
P
DC
)
cl
u
s
ter
i
n
g
is
e
m
p
lo
y
ed
w
ith
E
M
D
an
d
L
ea
s
t
S
u
p
p
o
r
t
Sq
u
ar
e
Vec
to
r
Ma
ch
in
e
(
L
S
SVM)
to
f
o
r
ec
ast
ex
ch
a
n
g
e
r
ate.
Fro
m
th
e
s
t
u
d
ies,
clu
s
ter
i
n
g
g
e
n
er
all
y
i
n
cr
ea
s
e
s
th
e
ac
cu
r
ac
y
o
f
th
e
f
o
r
ec
asts
.
Fu
zz
y
c
-
m
ea
n
s
cl
u
s
ter
i
n
g
h
as
also
b
ee
n
i
m
p
le
m
en
ted
in
f
o
r
ec
asti
n
g
[
2
1
]
.
Ho
w
e
v
er
,
cu
r
r
en
tl
y
t
h
er
e
ar
e
n
o
s
tu
d
ies
d
o
n
e
o
n
en
s
e
m
b
l
e
f
o
r
ec
asti
n
g
b
ased
o
n
c
-
m
ea
n
s
clu
s
ter
i
n
g
.
T
h
e
o
b
j
ec
tiv
e
o
f
th
is
r
esear
c
h
p
ap
er
to
in
v
esti
g
ate
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
AR
I
M
A
,
E
W
T
AR
I
M
A
an
d
m
o
d
i
f
ied
E
W
T
-
A
R
I
M
A
w
i
th
f
u
zz
y
c
-
m
ea
n
s
cl
u
s
ter
i
n
g
r
eg
a
r
d
in
g
to
th
eir
p
er
f
o
r
m
an
ce
i
n
f
o
r
ec
asti
n
g
d
r
o
u
g
h
t
u
s
i
n
g
Sta
n
d
ar
d
P
r
ec
ip
itatio
n
I
n
d
ex
(
SP
I
)
.
SP
I
3
,
SP
I
6
,
SP
I
9
,
SP
I
1
2
an
d
SP
I
2
4
w
er
e
u
s
ed
as
in
d
icato
r
f
o
r
s
h
o
r
t
ter
m
an
d
lo
n
g
-
ter
m
d
r
o
u
g
h
t.
Me
a
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
a
n
d
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
w
er
e
u
ti
lized
to
m
ea
s
u
r
e
th
e
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
.
AR
I
M
A
is
w
id
el
y
u
s
ed
in
d
r
o
u
g
h
t
f
o
r
ec
asti
n
g
,
h
o
w
ev
er
it i
s
n
o
t a
b
le
to
ac
cu
r
atel
y
f
o
r
ec
ast d
r
o
u
g
h
t b
ec
au
s
e
AR
I
M
A
d
o
es n
o
t e
x
ce
l a
t
n
o
n
-
li
n
ea
r
d
ata,
w
h
ic
h
th
e
SP
I
d
ata
ar
e.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
St
a
nd
a
rd
P
re
cipita
t
io
n In
de
x
T
h
e
Stan
d
ar
d
P
r
ec
i
p
itatio
n
I
n
d
ex
(
SP
I
)
w
a
s
d
ev
elo
p
ed
b
y
Mc
Kee
et
al.
[
2
2
]
as
a
d
r
o
u
g
h
t
in
d
icato
r
w
h
ic
h
s
ta
n
d
ar
d
s
th
e
r
ai
n
f
al
l e
x
ce
s
s
/d
ef
icit o
n
te
m
p
o
r
al
an
d
r
eg
io
n
al
b
asi
s
.
T
o
en
ab
le
q
u
an
ti
f
y
in
g
p
r
ec
ip
itatio
n
d
ef
icits
o
f
m
u
ltip
le
ti
m
e
s
ca
l
es,
m
a
n
y
d
r
o
u
g
h
t
s
t
u
d
ies
u
s
ed
SP
I
.
T
h
ese
ti
m
e
s
ca
le
s
s
h
o
w
t
h
e
i
m
p
ac
t
o
f
d
r
o
u
g
h
t
o
n
t
h
e
av
a
ilab
ilit
y
o
f
th
e
d
if
f
er
en
t
w
ater
r
eso
u
r
ce
s
.
R
eq
u
ir
i
n
g
o
n
l
y
r
ec
o
r
d
s
o
f
p
r
ec
ip
itatio
n
,
SP
I
en
ab
les
an
an
a
l
y
s
t
to
d
eter
m
in
e
t
h
e
r
ar
it
y
o
f
a
d
r
o
u
g
h
t
o
r
a
n
o
n
-
t
y
p
ical
w
et
e
v
en
t
r
el
ativ
e
to
a
p
ar
ticu
lar
ti
m
e
s
ca
le
a
n
d
ca
n
b
e
u
s
ed
w
o
r
ld
w
id
e
[
2
3
]
.
A
d
r
o
u
g
h
t
ev
e
n
t
o
cc
u
r
s
d
u
r
in
g
t
h
e
ti
m
e
w
h
e
n
S
P
I
is
co
n
tin
u
o
u
s
l
y
n
eg
at
iv
e.
T
h
e
d
r
o
u
g
h
t
e
v
en
t
en
d
s
w
h
en
t
h
e
SP
I
tu
r
n
s
p
o
s
itiv
e.
T
ab
le
1
s
h
o
w
s
t
h
e
SP
I
b
ased
d
r
o
u
g
h
t
class
i
f
icatio
n
.
T
ab
le
1
.
Dr
o
u
g
h
t Cl
a
s
s
i
f
icatio
n
u
s
in
g
SP
I
S
P
I
V
a
l
u
e
C
a
t
e
g
o
r
y
H
i
g
h
e
r
t
h
a
n
2
.
0
0
W
e
t
(
Ex
t
r
e
me
)
1
.
5
0
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n
t
i
l
1
.
99
W
e
t
(
S
e
v
e
r
e
)
1
.
0
0
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n
t
i
l
1
.
4
9
W
e
t
(
M
o
d
e
r
a
t
e
)
-
0
.
9
9
u
n
t
i
l
0
.
9
9
N
e
a
r
N
o
r
mal
-
1
.
4
9
u
n
t
i
l
-
1
.
0
0
D
r
y
(
M
o
d
e
r
a
t
e
)
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1
.
9
9
u
n
t
i
l
-
1
.
5
0
D
r
y
(
S
e
v
e
r
e
)
L
e
ss t
h
a
n
-
2
.
0
0
D
r
y
(
Ex
t
r
e
me)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
11
,
No
.
3
,
Sep
tem
b
er
2
0
1
8
:
1
1
5
2
–
1
1
6
1
1154
2
.
1
.
1
.
ARIM
A
Scien
ti
f
ic
ap
p
licatio
n
s
o
f
ti
m
e
s
er
ies
m
o
d
el
h
av
e
b
ee
n
w
id
e
s
p
r
ea
d
,
h
o
w
e
v
er
,
it
h
as
b
ee
n
l
i
m
ited
in
its
ap
p
licatio
n
in
d
r
o
u
g
h
t
f
o
r
ec
asti
n
g
[
3
]
.
T
h
e
ad
v
an
tag
e
s
o
f
u
s
in
g
ti
m
e
s
er
ies
m
o
d
el
ar
e
th
eir
id
en
ti
f
icatio
n
,
esti
m
atio
n
,
an
d
d
iag
n
o
s
tic
c
h
ec
k
ca
p
ab
le
o
f
s
y
s
te
m
atic
s
ea
r
ch
f
o
r
m
o
d
el
d
ev
elo
p
m
en
t
[
2
4
]
.
On
e
o
f
th
e
co
m
m
o
n
l
y
-
u
s
ed
ti
m
e
s
er
ies
m
o
d
el
i
s
Au
to
r
eg
r
es
s
i
v
e
I
n
te
g
r
ated
Mo
v
in
g
A
v
er
a
g
e
(
AR
I
MA
)
.
AR
I
M
A
is
p
o
p
u
lar
d
u
e
to
its
s
tati
s
tica
l
p
r
o
p
er
ties
an
d
its
ab
ilit
y
to
i
m
p
le
m
e
n
t
v
ar
io
u
s
ex
p
o
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2
5
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.
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2
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2
.
E
m
pirica
l Wa
v
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ra
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ir
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e
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ef
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ass
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en
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ch
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eg
m
e
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t
,
-
an
d
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,
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T
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e
E
eq
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atio
n
(
3
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d
(
4
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f
o
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w
s
d
e
f
i
n
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th
e
e
m
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s
ca
li
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u
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d
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w
a
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1
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m
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w
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etail
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s
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5
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T
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ap
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6
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tr
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/
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
F
o
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Dro
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1155
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2
.
3
.
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WT
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T
b
o
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k
f
r
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w
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a
n
s
f
o
r
m
[
6
]
.
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h
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s
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t
h
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u
r
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1
s
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o
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e
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r
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e
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k
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M
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e
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ased
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m
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le
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ted
E
W
T
[
7
]
,
[
1
6
]
,
[
2
6
]
,
th
e
m
o
d
e
th
a
t is co
m
m
o
n
l
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f
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r
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3
.
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w
e
v
er
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in
[
2
7
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th
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ates
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s
f
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p
er
f
o
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m
in
g
E
W
T
[
1
5
]
:
1.
P
er
f
o
r
m
p
r
e
-
p
r
o
ce
s
s
i
n
g
to
d
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t p
ea
k
.
2.
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er
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tr
u
m
s
ep
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ased
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n
th
e
d
etec
ted
m
ax
i
m
a
.
3.
C
o
n
s
tr
u
ct
co
r
r
esp
o
n
d
in
g
w
av
e
let
f
ilter
b
an
k
.
4.
C
o
n
s
tr
u
ct
t
h
e
w
av
e
lets
b
ased
o
n
th
e
w
a
v
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f
ilter
b
an
k
.
Fig
u
r
e
1
.
Ov
er
all
w
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k
f
lo
w
f
o
r
E
W
T
-
AR
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M
A
2
.
4
.
H
ilb
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ra
ns
f
o
r
m
Hilb
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t tr
an
s
f
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r
m
is
d
e
f
in
ed
as
f
o
llo
w
in
g
:
̃
(
)
∫
(
)
(
9
)
Whe
r
e p
i
s
t
he Cau
chy
pr
i
n
ci
pa
l
v
al
ue.
T
he
i
ns
t
an
t
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ous f
r
equ
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(
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t
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s c
an
be
ca
l
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ul
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us
i
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(
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, w
her
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̃
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(
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.
2
.
5
.
F
uzzy
C
-
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Cl
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c
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m
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FC
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ter
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it
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m
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eter
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r
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it
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o
b
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tiv
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u
n
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f
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ith
m
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ip
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u
clid
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s
tan
c
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(
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∑
̂
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(
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN
:
2
5
0
2
-
4752
I
n
d
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n
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n
J
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lec
E
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g
&
C
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m
p
Sci,
Vo
l.
11
,
No
.
3
,
Sep
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b
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2
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1
8
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1
1
5
2
–
1
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1
1156
(
1
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w
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‖
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s
[
2
8
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∑
(
‖
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(
1
1
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w
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m
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m
b
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ip
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pl
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h c
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.
‖
‖
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f
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.
2
.
6
.
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WT
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A
Clus
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ly
s
is
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e
o
v
er
all
f
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am
e
w
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r
k
f
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th
e
p
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p
o
s
ed
m
eth
o
d
is
as
in
F
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g
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r
b
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m
et
h
o
d
s
,
f
ir
s
tl
y
th
e
SP
I
d
ata
is
d
ec
o
m
p
o
s
ed
i
n
to
s
e
v
e
r
al
m
o
d
es
u
s
i
n
g
E
W
T
.
Nex
t,
I
MFs
ar
e
co
n
s
tr
u
cted
ea
ch
o
f
t
h
e
m
o
d
es
f
r
o
m
E
W
T
.
Hilb
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t
tr
an
s
f
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m
ar
e
d
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n
ea
c
h
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f
t
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to
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s
f
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w
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t
h
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y
w
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ll
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s
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s
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-
m
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s
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u
s
ter
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g
.
Fro
m
th
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o
b
tai
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clu
s
ter
s
,
3
s
u
b
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ies
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c
r
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ted
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y
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m
m
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th
e
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MF
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co
r
d
in
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itted
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th
m
et
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s
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in
al
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Fi
g
u
r
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2
s
h
o
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er
all
p
r
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s
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f
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r
E
W
T
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AR
I
M
A
C
lu
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ter
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al
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.
Fig
u
r
e
2
.
Ov
er
all
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f
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w
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o
d
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.
7
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al
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ated
b
y
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m
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ar
i
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e
er
r
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r
s
tati
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tics
.
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h
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er
r
o
r
s
tatis
t
ics
u
s
ed
w
er
e
M
A
E
an
d
R
M
SE.
T
h
e
y
ar
e
g
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n
b
y
:
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|
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(
1
2
)
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(
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(1
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W
h
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d
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en
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te
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s
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.
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h
e
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o
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er
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r
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2.
8
.
Study
Are
a
T
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p
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ical
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ea
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v
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is
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A
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P
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T
h
e
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ata
c
o
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s
is
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o
f
d
ail
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ain
f
al
l v
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u
e
f
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o
m
1
9
5
6
to
2
0
0
8
.
(
)
∑
(
)
(
)
X
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
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[
5
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io
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ad
e
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b
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M
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Fo
r
A
R
I
M
A
,
t
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ar
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th
r
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er
ies
m
o
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el
d
ev
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m
en
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n
a
m
e
l
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:
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tif
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,
esti
m
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,
a
n
d
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o
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ck
[
2
9
]
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r
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SP
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ca
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d
ata
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et
f
r
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e
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o
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.
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f
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eter
m
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h
e
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,
6
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s
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y
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SP
I
is
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lcu
lated
f
r
o
m
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e
r
ain
f
all
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ata.
Fig
u
r
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3
s
h
o
w
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th
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ata
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n
s
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g
o
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I
3
,
6
an
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.
Fig
u
r
e
3
.
SP
I
3
d
a
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er
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m
1
9
5
6
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2
0
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8
Fig
u
r
e
2
s
h
o
w
s
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au
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r
r
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n
f
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n
c
tio
n
(
A
C
F)
an
d
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r
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n
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ctio
n
(
P
AC
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u
s
ed
o
f
SP
I
3
.
Fro
m
th
e
AC
F
a
n
d
P
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C
F
p
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e
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itab
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M
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e
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ar
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d
0
.
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h
e
co
m
b
in
atio
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els
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le
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e
o
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f
r
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n
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th
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els d
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m
i
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ed
.
Fig
u
r
e
4
.
AC
F a
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d
P
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f
S
P
I
3
T
o
s
elec
t
th
e
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est
f
it
m
o
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Ak
ai
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n
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atio
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A
I
C
)
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.
T
h
e
b
est
f
it
m
o
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is
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ted
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m
th
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t
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.
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h
e
m
a
th
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atica
l f
o
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m
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la
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is
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ef
i
n
ed
as
[
3
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]
:
(
1
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w
h
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e
(
)
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n
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m
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h
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r
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eter
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i
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e
w
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e
i
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ad
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Fi
g
u
r
e
5
s
h
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t
h
e
d
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ch
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k
o
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I
M
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m
o
d
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tain
ed
f
o
r
SP
I
3
.
3
.
2
.
E
WT
-
ARIM
A
E
W
T
is
u
s
ed
to
d
ec
o
m
p
o
s
e
th
e
SP
I
d
ata
in
to
s
e
v
er
al
I
MF.
T
h
e
n
u
m
b
er
o
f
I
M
F
d
ep
en
d
s
o
n
th
e
d
ata
,
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e
it
is
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eter
m
i
n
ed
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s
in
g
Ots
u
’
s
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et
h
od
[
2
7
]
.
T
ab
le
3
s
h
o
w
s
t
h
e
n
u
m
b
er
o
f
I
MFs
g
en
er
ated
b
y
E
W
T
p
r
o
ce
s
s
.
Fig
u
r
e
6
s
h
o
w
s
SP
I
1
2
d
ec
o
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o
s
ed
to
6
I
MFs
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s
i
n
g
E
W
T
.
A
R
I
M
A
m
o
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u
s
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f
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d
in
R
,
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n
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i
t
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s
ed
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er
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o
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m
t
h
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f
o
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h
e
r
e
s
u
lt
o
f
t
h
e
f
o
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ec
ast
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h
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ies
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h
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h
en
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s
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o
n
s
tr
u
ct
t
h
e
o
r
ig
in
al
t
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m
e
s
er
ies.
T
h
e
r
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lt o
f
th
e
r
ec
o
n
s
tr
u
c
tio
n
is
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h
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f
o
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ast r
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lt o
f
E
W
T
-
AR
I
M
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.
Fig
u
r
e
6
.
E
W
T
d
ec
o
m
p
o
s
itio
n
o
f
SP
I
1
2
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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d
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J
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m
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2502
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4752
F
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Dro
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in
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3
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ter
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ter
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f
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m
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er
o
f
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ter
.
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h
e
n
u
m
b
er
o
f
clu
s
ete
r
ch
o
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en
is
3
.
Sev
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e
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ed
3
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ter
[
1
7
]
,
[
1
8
]
.
Fig
u
r
e
7
s
h
o
w
s
th
e
co
m
b
i
n
ed
in
to
3
w
a
v
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ts
b
a
s
ed
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n
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lt
o
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ter
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.
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MF
1
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2
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co
m
b
i
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f
o
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h
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lo
w
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r
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en
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m
en
t.
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3
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4
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5
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m
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in
ed
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o
r
m
th
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Gr
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t V
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[1
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[6
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b
d
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―
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‖
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.
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d
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.
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i
.
,
v
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1
,
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o
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4
,
p
p
.
1
5
3
–
1
5
8
,
2
0
1
2
.
[7
]
J.
Hu
a
n
d
J.
W
a
n
g
,
―
S
h
o
rt
-
term
w
in
d
sp
e
e
d
p
re
d
icti
o
n
u
sin
g
e
m
p
iri
c
a
l
w
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let
tran
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m
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d
Ga
u
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ro
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re
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9
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1
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6
–
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4
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6
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c
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2
0
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5
.
[8
]
V
.
No
u
ra
n
i,
A
.
Ho
ss
e
in
i
Ba
g
h
a
n
a
m
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A
d
a
m
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i,
a
n
d
O.
Ki
si,
―
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p
p
li
c
a
ti
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o
f
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y
b
rid
w
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let
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rti
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In
telli
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m
o
d
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ls
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h
y
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ro
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y
:
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re
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w
,
‖
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.
Hy
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l.
5
1
4
,
p
p
.
3
5
8
–
3
7
7
,
J
u
n
.
2
0
1
4
.
[9
]
S
.
S
c
h
l
ü
ter an
d
C.
De
u
sc
h
le,
―
Us
in
g
W
a
v
e
lets
f
o
r
T
i
m
e
S
e
ries
F
o
re
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a
stin
g
–
Do
e
s it
P
a
y
Off
?
,
‖
2
0
1
0
.
[1
0
]
T
.
Krie
c
h
b
a
u
m
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r,
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.
A
n
g
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s,
D.
P
a
rso
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s,
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d
M
.
Riv
a
s
Ca
sa
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o
,
―
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im
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w
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v
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let
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a
p
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ro
a
c
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f
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f
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m
e
tal
p
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s,‖
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,
v
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l
.
3
9
,
p
p
.
3
2
–
4
1
,
M
a
r.
2
0
1
4
.
[1
1
]
R.
V.
Ra
m
a
n
a
,
B.
Krish
n
a
,
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.
R.
Ku
m
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r,
a
n
d
N.
G
.
P
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n
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e
y
,
―
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o
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ly
Ra
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a
ll
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Us
in
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Ne
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n
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ter
Res
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v
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1
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p
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3
6
9
7
–
3
7
1
1
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u
g
.
2
0
1
3.
[1
2
]
Y.
P
.
L
iu
,
Y.
W
a
n
g
,
a
n
d
Z.
W
a
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g
,
―
RBF
P
re
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M
o
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l
Ba
se
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‖
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v
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.
,
v
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l.
7
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5
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p
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2
8
3
0
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8
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0
1
3
.
[1
3
]
Z.
G
u
o
,
W
.
Z
h
a
o
,
H.
L
u
,
a
n
d
J.
W
a
n
g
,
―
M
u
lt
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ste
p
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stin
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sp
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ra
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m
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‖
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w.
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3
7
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1
,
p
p
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2
4
1
–
2
4
9
,
Ja
n
.
2
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1
2
.
[1
4
]
Y.
Y.
U.
G
u
a
n
g
,
Z.
Y.
U.
Hu
,
a
n
d
X
.
S
.
I.
o
f
B.
a
n
d
T
.
L
iu
,
―
A
N
o
v
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l
S
trate
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in
d
S
p
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e
d
P
re
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in
W
i
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d
F
a
rm
,
‖
T
EL
KOM
NIKA
In
d
o
n
e
s.
J
.
El
e
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tr.
En
g
.
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v
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1
1
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1
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p
p
.
7
0
0
7
–
7
0
1
3
,
2
0
1
3
.
[1
5
]
J.
G
il
les
,
―
E
m
p
iri
c
a
l
W
a
v
e
let
T
r
a
n
sf
o
r
m
,
‖
IEE
E
T
ra
n
s.
S
ig
n
a
l
P
ro
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.
,
v
o
l.
6
1
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1
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p
p
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3
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9
9
–
4
0
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0
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A
u
g
.
2
0
1
3
.
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1161
[1
6
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J.
Hu
,
J.
W
a
n
g
,
a
n
d
K.
M
a
,
―
A
h
y
b
rid
tec
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n
iq
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e
f
o
r
sh
o
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term
w
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7
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.
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g
h
a
b
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A
.
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y
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d
S
h
irk
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rsh
id
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n
d
T
.
Yin
g
W
a
h
,
―
T
i
m
e
-
se
ries
c
lu
ste
rin
g
–
A
d
e
c
a
d
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re
v
iew
,
‖
In
f.
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st.
,
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5
3
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o
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C,
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1
6
–
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8
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2
0
1
5
.
[1
8
]
A
.
S
h
a
b
ri,
―
A
m
o
d
if
ied
EM
D
-
ARIMA
b
a
s
e
d
o
n
c
lu
ste
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g
a
n
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ly
sis
f
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‖
v
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1
0
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p
p
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1
7
1
9
–
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7
2
9
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2
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1
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[1
9
]
N.
I.
A
.
Ra
sh
id
,
R.
S
a
m
su
d
in
,
a
n
d
A
.
S
h
a
b
ri,
―
Ex
c
h
a
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g
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Ra
te
F
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Us
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M
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p
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In
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Ad
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l.
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.
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0
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N.
I.
A
.
Ra
sh
id
,
A
.
S
h
a
b
ri,
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n
d
R.
S
a
m
su
d
in
,
―
COM
P
A
RIS
ON
BET
W
EE
N
M
EM
D
-
L
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M
A
ND
M
EM
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RIM
A
IN
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TING
EX
CHA
N
G
E
R
AT
E,
‖
J
.
T
h
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o
r.
Ap
p
l.
In
f.
T
e
c
h
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o
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,
v
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9
5
,
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o
.
2
,
p
.
3
2
8
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0
1
7
.
[2
1
]
J.
Na
y
a
k
,
B.
Na
ik
,
a
n
d
H.
S
.
Be
h
e
ra
,
―
F
u
z
z
y
C
-
M
e
a
n
s
(F
CM
)
Clu
ste
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g
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l
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m
:
A
De
c
a
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Re
v
iew
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ro
m
2
0
0
0
to
2
0
1
4
,
‖
i
n
Co
m
p
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t
a
ti
o
n
a
l
I
n
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0
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5
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p
.
1
3
3
–
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.
[2
2
]
T
.
B.
M
c
K
e
e
,
N.
J.
Do
e
s
k
e
n
,
J.
Kle
ist,
a
n
d
o
th
e
rs,
―
T
h
e
re
latio
n
sh
ip
o
f
d
ro
u
g
h
t
f
re
q
u
e
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c
y
a
n
d
d
u
ra
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n
to
ti
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e
sc
a
les
,
‖
in
Pro
c
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d
in
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s
o
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8
th
Co
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Ap
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1
7
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.
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.
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3
]
A
.
K.
M
ish
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a
n
d
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.
R.
De
sa
i,
―
Dro
u
g
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4
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A
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K.
M
ish
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.
R.
De
sa
i,
―
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u
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F
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Us
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S
to
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to
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.
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.
[2
5
]
M
.
S
h
a
f
ie
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.
P
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o
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m
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d
M
.
K.
S
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ik
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-
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-
Esla
m
i,
―
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rice
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o
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y
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6
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–
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1
6
9
,
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y
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0
1
1
.
[2
6
]
J.
W
a
n
g
a
n
d
J.
Hu
,
―
A
ro
b
u
st
c
o
m
b
in
a
ti
o
n
a
p
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ro
a
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sh
o
r
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term
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d
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ly
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y
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9
3
,
P
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1
,
p
p
.
4
1
–
5
6
,
De
c
.
2
0
1
5
.
[2
7
]
J.
G
il
les
a
n
d
K.
He
a
l,
―
A
p
a
ra
m
e
terle
s
s
sc
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le
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sp
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c
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p
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