) 7 1 0 2 A T S M M ( s n o it a c il p p A d n a s e i g o l o n h c e T n o it a l u m i S d n a g n il l e d o M , s c it a m e h t a M n o e c n e r e f n o C l a n o it a n r e t n I 7 1 0 2 8 7 9 : N B S
I -1-60595- 05 -8 3
s
e
u
q
i
n
h
c
e
T
g
n
i
n
r
a
e
L
e
n
i
h
c
a
M
n
o
d
e
s
a
B
d
l
o
G
f
o
e
c
ir
P
e
h
t
t
c
i
d
e
r
P
n
a
H -
c
h
a
o
Z
H
U
a
n
d
D
o
n
g
W
A
N
G
, y ti s r e v i n U g n o t o a i J i a h g n a h S , g n i r e e n i g n E e r a w t f o S f o l o o h cS Shangha,iChina
r o h t u a g n i d n o p s e r r o C * s d r o w y e
K :Machinelearning, Greymodel, Associaitonrulesmining, Prediciton.
t c a r t s b
A .Dueto theuniquehedging and investmen tfunctionsof thepreciousmetalsmarket ,the e h T . t e k r a m l a i c n a n i f l a n o i t a n r e t n i e h t n i e l o r e v i s i c e d a y a l p d l o g y b d e t n e s e r p e r s l a t e m s u o i c e r p n e t t a t a e r g n w a r d s a h e c i r p d l o g e h t f o t s a c e r o
f iton from investors ,governmen tdepartments and
s u o i c e r p f o e c i r p e h t g n it c i d e r p n i n o i t a v o n n i n a e d a m r e p a p s i h T . d l r o w e h t r e v o l l a s r e h c r a e s e r e n i h c a m a , e l p m a x e n a s a d l o g e h t g n i k a T . s l e d o m l a c i t s i t a t s l a n o i t i d a r t e h t o t l l e w e r a f g n i d d i b , s l a t e m n r a e
l ing method to completely predic tthe trend of the gold price and proved its reliabiltiy by e h T . s t n e m i r e p x
e experimenta lresultsshowt ha tourproposedmethodhasasignifican tadvantagei n d n a e l b a i l e r e r o m a s e d i v o r p d n a s t n e m e v o m e c i r p d l o g g n i t s a c e r o
f effecitveresul tthan any other
e r o f e b d e s o p o r p s d o h t e
m .Themain contributions of thispaper areasfollows :1 .Theassociation n i g n i n i m r o t c a f f o m e l b o r p e h t e v l o s o t d e i f i d o m d n a d e t s u j d a , d e c u d o r t n i s i m h t i r o g l a s e l u r o c e h t t a h t o s , s l e d o m e v it c i d e r
p mputer can mine the mos t time-efficien t influencing factors e c i r p d l o g e h t t c i d e r p y ll u f s s e c c u s o t d e s o p o r p s i l e d o m ) 1 , 1 ( M , G . t e s a t a d t s e t a l e h t o t g n i d r o c c a e d n e t x e e b y l i s a e n a c l e d o m d e s o p o r p r u o e c n i S . s r o t c a f e v o b a f o g n i n i m e h t h t i w g n i n i b m o c d n e r t d c i r p y t i d o m m o c r e h t o d n a x e d n i k c o t s s a h c u s s t e g r a t c i m o n o c e r e h t o f o s t s a c e r o f o
t es ,weexpec tour
d e l a c s e b o t d o h t e
m tomoreareasofforecasting.
n o it c u d o r t n I i c e p s e , t e k r a m s l a t e m s u o i c e r p e h t f o s n o i t c n u f t n e m t s e v n i d n a g n i g d e h e u q i n u e h t o t g n i w
O ally
w e n a o t n e s i r s a h s l a t e m s u o i c e r p n o n o it n e tt a l a n o i t a n r e t n i e h t , 8 0 0 2 f o s i s i r c l a i c n a n i f e h t e c n i s s r a e y 0 0 6 , 2 t u o b a s a y l r a e s A . d n i k n a m y b d e p o l e v e d l a t e m s u o i c e r p f o d n i k r e i l r a e n a s i d l o G . t h g i e h e h t r o f y c n e r r u c c i t s e m o d e h t e m a c e b d l o g , o g
a exchangeofmateria leconomy .Wheninternationa l
e m a c e b d l o g , d a e r p s e d i w e r o m e m o c e b e v a h s e i t i v i t c a c i m o n o c
e a worldwide currency for the
f o p o t e h t s a d e d r a g e r n e e b s y a w l a s a h d l o G . y c n e r r u c c i t s e m o d f o n o i t i s o p t n e n i m o r
p hardcurrency
o h s s t i f o e s u a c e
b r tsupply ,preciousness and chemica lstability .I talso plays a key role in the s u o i c e r p a s A . s e r u t a e f t n e n i m o r p s t i y b y t e i c o s n a m u h f o y ti l i b a t s l a i c o s d n a t n e m p o l e v e d c i m o n o c e a s n o i t c n u f s ti r o f n o i t n e tt a h c u m d i a p n e e b s a h t i , y c n e r r u c d r a h d n a l a t e
m smeasurevalue ,meansof
. n o o s d n a y c n e r r u c d l r o w d n a e g a r o t s f o s n a e m , n o i t a l u c r i c f o s n a e m , t n e m y a p t n e m n r e v o g , s r o t s e v n i m o r f n o i t n e t t a t a e r g n w a r d s a h e c i r p d l o g e h t f o t s a c e r o f e h t , e r o f e r e h T , r e v e w o H . d l r o w e h t r e v o l l a s r e h c r a e s e r d n a s t n e m t r a p e
d thefactorst ha taffectt hepriceofgoldare
e h t t c i d e r p o t y s a e t o n s i t I . d e v l o v n i s r o t c a f m o d n a r y n a m o s l a e r a e r e h T . x e l p m o c d n a s u o r e m u n e h t n o s e h c r a e s e r y n a m t u o d e i r r a c e v a h d a o r b a d n a e m o h t a s r a l o h c S . y l e t a r u c c a d l o g f o e c i r p e h t f o n o it c i d e r
p gold priceand haveproduced many excellen tresearch results .Asearly as1980 , t a h t s e r u t a e f s e i r e s e m i t g n o r t s d a h d n a e m it h t i w s e t a u t c u l f d l o g f o e c i r p e h t t a h t d n u o f ] 1 [ l g e o h c s T l o g e h t f o y c a r u c c a e h t e v o r p m i o t d e i r t s r a l o h c s e m o S . d e t c i d e r p e b n a
c dpriceforecas tbyanalyzing
s s e n i s u B ( . l a t e ] 2 [ a m a F , y r o e h t y r o t n e v n i e h t o t g n i d r o c c A . e c i r p d l o g e h t t c e f f a t a h t s r o t c a f e h t e h t d n a e l c y c c i m o n o c e e h t f o t c a p m i e h t d e i d u t s ) s e c i r P s l a t e M f o r o i v a h e B e h t d n a s e l c y C g e h t n o t n e m n o r i v n e c i m o n o c e o r c a
m oldprice .Throught het es toft hemargina lrevenue,t hereasons e g n a h c x e e h t d n a d l o g f o e c i r p e h T ( l a t e ] 3 [ y r r a L . d e z y l a n a e r e w e c i r p d l o g e h t f o n o i t a u t c u l f e h t r o f e n i a m e h t n e e w t e b p i h s n o it a l e r l a c i r i p m e d n a l a c it e r o e h t e h t d e t s e t ) n i a g a e c n O : s e t a
r xchangerate
s t l u s e r e h t f o s i s y l a n a l a c i t s i t a t s e h t m o r f d n u o f s a w t I . a t a d r o r r e t s a c e r o f e h t g n i s u s e c i r p d l o g d n a t a h t t u o g n i t n i o p , s r o t c a f r e h t o h t i w d e r a p m o c e c i r p d l o g e h t n o t c a p m i t s e t a e r g e h t s a h r a l l o d e h t t a h t s i h e h t m o r f e l b a r a p e s n i s a w s i h
t n a v e l e r e h t n o d e s a b s t h g i e w c i r t e m m y s a h t i w l e d o m y t i c i t s a d e k s o r e t e h l a n o i t i d n o c e v i s s e r g e r o t u a n i s u t c a p m i e h t e z y l a n a o t d n a , s e c i r p t o p s d n a s e r u t u f d l o g r o f e c n a n i f f o s e i r o e h
t g theothertwo
. s l e d o m r a e n i l d e s u y l n o m m o c e m o h t a d e p o l e v e d n e e b e v a h s l e d o m n o it c i d e r p y n a m , e c i r p d l o g f o t s a c e r o f e v i t a t i t n a u q e h t r o F . l e d o m y a r g d n a l e d o m s i s y l a n a n o i s s e r g e r , l e d o m s i s y l a n a s e i r e s e m i t s a h c u s , d a o r b a d n a a t e ] 5 [ i h c h c a r a a l a g i t i
P l .(ModelingandForecastingSr iLankanGoldPrices)se tupARIMAforecas t a t a d e c i r p d l o g y l h t n o m e h t n o d e s a b d l o g f o e c i r p e h t t s a c e r o f o t y l e v i t c e p s e r l e d o m R A V d n a l e d o m t e s e h t f o s t l u s e r n o i t c i d e r p e h t d n A . 4 1 0 2 y a M o t 5 0 0 2 y r a u n a J m o r f a k n a L i r S f
o wo modelsare
g n i v o M d e t a r g e t n I e v i s s e r g e R o t u A h t i w e c i r P d l o G g n it s a c e r o F ( . l a t e ] 6 [ y h t a p i r T . d e r a p m o c s e x e d n i n o i t a u l a v e e h T . l e d o m A A M R A e h t g n i s u a i d n I n i e c i r p d l o g e h t t s a c e r o f ) l e d o M e g a r e v A o r r e e r a u q s n a e m t o o r , ) E A M ( r o r r e e t u l o s b a n a e m s a h c u
s r (RMSE) ,maximum absolute error
e r e w ) E P A M ( r o r r e e g a t n e c r e p e t u l o s b a n a e m d n a ) E A x a M ( r o r r e e t u l o s b a m u m i x a m , e g a t n e c r e p f o y d u t S l e d o M l a c i t a m e h t a M ( . l a t e ] 7 [ n a u y n i X g n a Y . l e d o m e h t f o y c a r u c c a e h t e t a u l a v e o t d e s u n o c ) n o it c i d e r P e c i r P s l a t e M s u o i c e r
P structed the mathematica lmodels such as Markov model ,
t s a e l l a i t r a
p -squares analysis and artificia lneura lnetwork mode lin order to study the variation . s e c i r p l a t e m s u o i c e r p f o y t i r a l u g e
r Thepredictionaccuracyoft heneura lnetworkmodeli nt hestudy t s a e l l a i t r a p y b d e w o ll o f , t s e h g i h e h t s a
w -squares ,andt heMarkovmode lwast hewors.t
f o a e d i e h t h t i w y r o e h t y a r g f o s t p e c n o c d e t a l e r e h t e n i b m o c o t s i g n i n i m y a r g f o a e d i c i s a b e h T e t s y s e h t r o f m e t s y s g n i n i m e g d e l w o n k d e t a m o t u a n a m r o f d n a g n i n i m a t a
d mof"lesssamplesand
n w o n k n u d n a s r o t c a f n w o n k g n i v l o s f o m e t s y s e h t n i y t i l i b a c i l p p a d o o g s a h t I . " n o i t a m r o f n i r o o p a t a d f o d l e i f n o it a c i l p p a e h t n e d a o r b n a c g n i n i m a t a d d n a y r o e h t y a r g f o n o i t a n i b m o c e h T . s r o t c a f i d n a y c n e i c i f f e g n i n i m e v o r p m i , g n i n i
m mprovepredicitonaccuracy .Thedomesticresearchongray
n i g n i t l u s e r , s d l e i f y n a m n i m e t s y s y a r g f o s e i r o e h t d e t a l e r e h t d e i l p p a s a h d n a r e i l r a e d e t r a t s y r o e h t ] 8 [ n i m e L u G . n o o s d n a y g o l o c e y a r g , e r u t l u c i r g a y a r g s a h c u s s t c e j b u s y r a n i l p i c s i d r e t n i y n a
m (An
s a r a f s a d e i f i l p m i s e b n a c y r o e h t y a r g t a h t t h g u o h t ) y r o e h T d n a ) M G ( l e d o M y a r G f o s i s y l a n A t n e r r u c n i s m e l b o r p d n a s g n i m o c t r o h s e h t d n a m e t s y s y a r g f o e c n e s s e e h t t u o g n it n i o p , e l b i s s o p l a v r e t n i n o i t a r b il a c f o n o i t a n i m r e t e D ( ] 9 [ i l n u J g n a h Z . h c r a e s e
r for ATS tes tinstrumentsbased on
s m e l b o r p e h t t a g n i m i a l e d o m n o it c i d e r p ) 1 , 1 ( M G d e v o r p m i n a d e s o p o r p ) l e d o m y a r g d e v o r p m i . s t n e m u r t s n i t s e t S T A f o d o i r e p n o i t a r b i l a c e h t n i g n i t s i x e h c i h w , d e r o l p x e s a w g n i n r a e l e n i h c a m n o d e s a b n o i t u l o s a , r e p a p s i h t n
I usesthecombinationof
e h t t c i d e r p y l e v i t c e f f e d n a y l b a i l e r o t l e d o m n o i t c i d e r p y a r g d n a i r o i r p A m h t i r o g l a s e l u r n o it a i c o s s a , s tl u s e r l e d o m l a n o i t i d a r t e h t h t i w s t l u s e r l a t n e m i r e p x e r u o d e r a p m o c n e h t d n a , d l o g f o d n e r t e c i r p e v o r p m i t n a c i f i n g i s a g n i w o h
s men tin forecas taccuracy .Since ourproposed mode lcan easily be , s e c i r p y t i d o m m o c r e h t o d n a x e d n i k c o t s s a h c u s s t e g r a t c i m o n o c e r e h t o f o s t s a c e r o f e h t o t d e d n e t x e .t c e f f e s t i t r e x e o t d n a g n it s a c e r o f f o s a e r a e r o m o t d e d n e t x e e b o t d o h t e m r u o t c e p x e e w f o n o is s u c si
D AlgortihmI mplementa itonandExperimenta lResutls a
t a
D Sources
e h t f o e n O . m o c . g n i t s e v n i e t i s b e w a t a d l a i c n a n i f s u o m a f e h t m o r f s e m o c e l c i t r a s i h t n i a t a d e h t l l A r a a t a d l l a , p i h s n o i t a l e r e c r u o s n e p o s t i s i e s a b a t a d s i h t g n i s o o h c r o f s n o s a e
r e provided for free
4 2 a s a h t i , d n o c e S . s e c i v r e s e c a f r e t n i s a l l e w s a , s i s y l a n a d n a d a o l n w o
d -hour real-time updated
. e c i r p d l o g t o p s r o f s t n e m e r i u q e r g n i d a r t l a b o l g e h t t e e m n a c h c i h w , e l u d o m a t a d l a i c n a n i f t n I d n a l a t r o p l a i c n a n i f l a b o l g a , m o c . g n i t s e v n
I erne tbrand ,is comprised of 30 versions in 22
d n a s e t o u q g n i m a e r t s , s c i t y l a n a , s w e n g n i d i v o r p , S O i d n a d i o r d n A r o f s p p a e li b o m d n a s e g a u g n a l n o it i d e h c a E . s t n e m u r t s n i l a i c n a n i f d n a n o it a m r o f n i a t a d l a c i n h c e t , s t e k r a m l a i c n a n i f l a b o l g n o s t r a h c i w a s r e v o
c de range of financia linstruments including stocks ,bonds ,commodities ,currencies , e w , a t a d l a i c n a n i f s t i f o h t p e d d n a e z i s e h t t n u o c c a o t n i g n i k a T . s n o i t p o d n a s e r u t u f , s e t a r t s e r e t n i e h t l l a d e n i a t b o d n a t e g r a t g n i n i m r u o s a e s a b a t a d s i h t e s u o t d e d i c e
d goldspo tpricesofeacht rading
e t a r e g n a h c x e , s e i r t n u o c s u o i r a v f o s e c i r p x e d n i k c o t s e h t s a l l e w s a d n a 7 0 0 2 , 1 y r a u n a J m o r f y a d n o n , s e c i r
p -agricultura lindex ,consumer index and the opening price and closing pricewithin 10 e j b o s i s y l a n a n a s a , s r a e
n o it a i c o s s
A Rule sAlgortihmApriori
y l p p a e
W Apriori ,aclassicassociaiton rulesalgorithmof"diaperand beer"in ourthesisto digou t .
s r o t c a f e h t n e e w t e b s e l u r n o i t a i c o s s a g n o r t s e h
t ThemainreasonforchoosingAprioriisbecauseof
, n o i t a l u c l a c r o f s m e t i g n i n e e r c s d n a s m e t i n o i t c i r t s e r e r o m d d a o t t n e i n e v n o c s i t i , y t i l i b a l a c s h g i h s t i
m h t i r o g l a i r o i r p A f o l a o g e h T . n o i s n e t x e d n a t n e m e v o r p m i m h t i r o g l a e r u t u f r o f l a i c i f e n e b s i h c i h w
e m i t e h t m o r f t u o d n i f o t s
i -seriesdataofmanyeconomictargetstha t"thecorrespondingeconomic a h c u s g n i w o n k r e t f A " . s e s i r e c i r p d l o g e h t f i ) h t n o m ( s y a d s s e n i s u b N e h t n i % 5 7 y b s e s i r A t e g r a t
h t h ti w n o it a i c o s s a f o s e l u r g n o r t s e v a h t a h t s t e g r a t c i m o n o c e e z y l a n a d n a d n a p x e n a c e w , e l u
r eprice
t n e v m u c r i c o t r e d r o n i g n i n r a e l e n i h c a m f o s l e d o m o t t i y l p p a y l e t a r u c c a e r o m n a c e w t a h t o s d l o g f o
, e l i h w n a e M . s e s s e n i s u b d n a s e v i l r u o n i s e c i r p d l o g n o n o i t a u t c u l f f o s k s i r s u o i r a v e h
t sincemany
e r u t a e f s e i r e s e m i t r e g n o l e v a h s t e g r a t c i m o n o c
e s ,suchas"cpi ,gdp"andotheri ndicatorsarereleased h t n o m " d n a " y a d " e k a t o t d e d i c e d e w , l a v r e t n i a t a d f o y t i s r e v i d e h t e v l o s o t r e d r o n i , y l h t n o m t s a e l t a
r p e h t t c e f f a t a h t s r o t c a f e h t t u o g i d o t r e d r o n i , s t e s n o i t c a s n a r t f o t i n u a s a y l e v i t c e p s e r
" iceofgold.
: s e l u r c i f i c e p s f o m r o f e h T
𝑃1(𝑥𝑖,𝑡𝑖)^𝑃2(𝑥𝑖,𝑡𝑖)^𝑃3(𝑥𝑖,𝑡𝑖)^𝑃𝑛(𝑥𝑖,𝑡𝑖)→𝐺(𝑥𝑖,𝑡𝑖) ( 1)
e h t s t n e s e r p e r P d n a , y d u t s r u o f o k r a m h c n e b c i m o n o c e e h t , d l o g f o e c i r p e h t s t n e s e r p e r G : e r e h W
u t a t
s sofeachoftheothereconomictargets 𝑥𝑖 in thetransaciton itema tthistime(day ormonth) , .
s t u p t u o s u t a t s e e r h t e n i f e d e w e r e h w
n e h W : n o i t i n i f e
D 𝑥𝑖 isup ,ont hesameday 𝑡𝑖,t heresul tofclosingi ndex 𝑃𝑖1 minust heopening e
d n
i x 𝑃𝑖0 ofeconomict arge t𝑃𝑖 ispositiveand �Zi
Pi0�≥�z� �%,t heresul tof 𝑥𝑖 isup ,where �z� �% is
n e h w , y r a r t n o c e h t n O . t e g r a t c i m o n o c e e h t f o e d u t il p m a e g a r e v a y l i a d e h
t 𝑥𝑖 isdown ,ontheday
𝑡𝑖 ,the resul tof the closing index 𝑃𝑖1 minus the opening index 𝑃𝑖0 of economic targe t Pi is
f o t l u s e r e h t , e v i t a g e
n 𝑥𝑖 isnegative .Inanyoft heabovecases ,when �Zi
Pi0�� �z� �% ,t hevolatilityi s
f o t l u s e r g n i d n o p s e r r o c e h t , h g u o n e e g r a l t o
n 𝑥𝑖 is0 ,ie ,themissing item 𝑃𝑖 in theaboveformula
. e c n a v e l e r o n s a h
, t s r i f e l u r e h t f o d n e t h g i r e h t t a m e ti e h t e t a l u c l a c e n i h c a m e h t t e l e w , n o i t a r e p o e h t n i , e r o f e r e h T
s i t a h
t G(xi,ti) ,when �Zi
Pi0�� �z� �% ,these tofthingsontheday(month) canbeeliminated by the
.t s o c n o i t a r e p o m h t i r o g l a e h t s e c u d e r y l t a e r g h c i h w , t s r i f m e t s y s
g n i y l p p
A theoperationoft heaboverestricitonsfromtheAprior ialgorithm ,weonlyconsidert he s
i s a b y l i a d a n o a t a
d ,shieldingou tthetimeinterval property .I tcan directlygeneratefrequen titem s
t e
s Ln ,which is generated in the occurrence of "up" or "down" situation of G(xi,ti) under the f
i , t e s g n i d n o p s e r r o
c Ln−1 isasubse tof Ln ,thesystem wil lautomaitcally remove f Ln−1 ,and a t e
m i t e m a s e h
t .After the generation of Ln ,we only need to delete the record of suppor t� the ,
t r o p p u s m u m i n i
m thenwecangett wof requenti temsetsof G(xi,ti) ,as"up"or"down"r espectively , Li and Ld ,in which Li represen tthe frequen t tiemsets when the gold price goes up and Ld
f o t e s a s i e r e h t , e c n e d i f n o c f o e s a c e h t n I . s p o r d e c i r p d l o g e h t n e h w s t e s m e t i t n e u q e r f e h t s t n e s e r p e r
r t s e r a t a h t s r o t c a
f onglyrelatedt ot hecorrespondingeconomici ndicators .Att hesamet ime ,sincet he s n o i t a l e r r o c e v i t a g e n d n a e v i t i s o p e z y l a n a n a c e w , " n w o d " r o " p u " s a d e n i f e d s i s r o t c a f e h t f o s u t a t s
t c n i t s i d m o r
f 𝐿𝑖 and 𝐿𝑑.
f o r e b m u n e g r a l a , t n e m i r e p x e s i h t n
I targe teconomicdatafromJanuary2007 t oNovember2017
g n i d u l c n i , d e t c e l e s e r e
w various spo tprices ,currency index ,cp iindex of different countries ,the t
s a d l o g h ti w , . c t e , s c i m o n o c e f o t c e j b u
s heeconomictarget ,wedig ou tstrong correlaiton factors
e h T . e c n e d i f n o c % 0 6 d n a t r o p p u s % 0 2 s a t e s s i m h t i r o g l a i r o i r p A e h t h c i h w g n o m a , t i o t d e t a l e r
e m i t e h t o t e u d s i g n i t t e s s i h t r o f n o s a e
r -dependentnessofmany economicindicators ,forexample , e
h
t stock index doesno tscrol lon weekendsandholidays ,bu tthepriceofgoldisrolling daliy ,we .
s m e l b o r p e v o b a e h t d i o v a o t r e d r o n i % 0 6 o t e c n e d i f n o c e h t e c u d e
r Att hesamet ime,t aking"daily"
h t n o m " d n
a ly" astheuntisofthetransaction set ,wetry to find ou tthefactorstha taffec tthegold d
n a t r o h s e h t n i y l e v i t c e p s e r e c i r
p longterm.
e r a s t l u s e r l a t n e m i r e p x e e h
. a t a D y l i a D g n i n i M f o y r a m m u S . 1 e l b a T a
d ( s e l u
R i ) ly Ppetroleum P s r a l l o
d pP ork P brass SP &P Confidence r
s e c i r p d l o
G i es x 0 x 1 x 1 x 0 x 1 72.70%
Goldpricefa ll x 1 x 0 x 0 x 1 x 0 69.30%
e li h w , d l o g f o e c i r p e h t n i e g n a h c e h t o t d e t a l e r y l e v it i s o p s i e l b a i r a v e h t t a h t s n a e m 0 x , e l b a t e h t n I
a c e W . d e t a l e r y l e v i t a g e n s i 1 x y h t a p m
e n find ou tthrough the applicaiton and modification of
t r o h s w e n e h t r e v o c s i d o t d e s i r p r u s e r a e w , s m h t i r o g l a g n i n r a e l e n i h c a
m -termi mpac tofgoldprices ,
c i m o n o c e l a n o i t i d a r t y b d e r e v o c s i d s r o t c a f e h t , e m i t e m a s e h t t A . s e c i r p s s a r b d n a s e c i r p k r o p
s y l a n
a is ,such as oil ,dollar and stock index should be testified ,which verifies the accuracy and .
m h t i r o g l a i r o i r p A e h t f o y t il i b a t s
. a t a D y l h t n o M g n i n i M f o y r a m m u S . 2 e l b a T (
s e l u
R monthly) P m u e l o r t e
p dP ollars P brass UP SCPI Confidence e
s i r s e c i r p d l o
G x 0 x 1 x 0 x 0 75.40%
l l a f e c i r p d l o
G x 1 x 0 x 1 x 1 74.70%
s a h l e v e l e c n e d i f n o c e h t t a h t e e s n a c e w , m r e t g n o l d n a m u i d e m e h t n i g n i n i m a t a d e h t m o r F
g n o l d n a m u i d e m e h t t a h t s e t a c i d n i h c i h w , l a v r e t n i e m it e h t f o n o i s n e t x e e h t h t i w r e h g i h e m o c e
b
-r t c a p m i m r e
t ulesare morepowerful .Forshort-term factors ,taking pork as an example ,theday's g n o l e h t n i t u b , n o i t a l e r r o c g n o r t s f o t r o s e m o s s i e r e h t , d l o g f o e c i r p e h t h t i w e n i l n i e b y a m e c i r p
d n i f o s l a e w , n o it i d d a n I . r e w o p y r o t a n a l p x e g n o r t s o n s i e r e h t , n u
r tha ttheopitmisminthemarke t
t i e z y l a n a e w r e h t e h W . e c n a l i g i v e h t n a h t r e t a e r g s y a w l a s i n o i t c a e
r ondaily ormonthlybasis ,i tis
r e v o y l t h g i l s e b l l i w t n e m i t n e s t e k r a m t a h t d n i f o t t l u c i f f i d t o
n -reacted when i tisup .Thisexplains
t a n c i t s i m i t p o e h
t ureofi nvesting.
) 1 , 1 ( M G l e d o M y a r G
r o s e t a l u m u c c a t I . n o i t c i d e r p y a r g f o e l p i c n i r p c i s a b e h t n o d e s a b s i l e d o m n o i t c i d e r p y a r g e h T
f o l e d o m n o i t c i d e r p a d n i f o t n o i t a r e n e g e c n e u q e s y a r g s e s u d n a , e c n e u q e s a t a d l a n i g i r o e h t s t c a r t b u s
a n r e t n i s ' m e t s y s e h
t lregularity .TheGM(1,1)mode lisoneofthemorecommonly usedalgorithms t
s r i f d n a e t a i r a v i n u a s t n e s e r p e r t I . s l e d o m n o i t c i d e r p y a r g n
i -order gray prediction model .The
: s w o l l o f s a e r a s p e t s g n i l e d o m
e c n e u q e s a t a d l a n i g i r o e h t t c e l e s , p e t s t s r i f e h T
Thegraypredictionmodeli sapredictionmethodsuitableforl essdata .Thenonnegativedataofa e
c n e u q e s a t a d l a n i g i r o e h t s a m e t s y s d e t c e l e
s P(0) isdenotedas:
{
(0) (0) (0) (0)}
) 0
( P (1),P (2),...P (i)...P (n)
P = ( 2)
e h
T secondstep ,gradej udgment : s a d e n i f e d s i e c n e u q e s e h t f o e d a r g e h T
-) 0 (
) 0 (
) j (
（j）=
) 1 j (
P P
σ ( 3)
: s a d e n i f e d s i e c n e u q e s e h t f o o i t a r h t o o m s e h T
) 0 ( 1
1 ) j ( （j）=
) i ( j
i
P P
ρ −
= ∑
) 4 (
: s e i f s i t a s e d a r g s ti f i , e c n e u q e s a r o F
(a )Foranyj ,1>σ（j）>0,t hent hesequencehasanegativegrayexponenitall aw. (b )Foranyj ,s>σ（j）>0 ,ands>1,t hesequencehasapositiveasymptoticexponen.t
(c )Foranyj ,s>σ（j）>q ,ands-q=Ψ,t hesequencei saquasi-indexl awwithabsolutegrayΨ.
i s a u q a d e l l a c s i e c n e u q e s a , s o it a r h t o o m s r o
F -smoothsequenceifi tsatisfiesthefollowingthree
: s n o i t i d n o c
(a ) （j+1） 1 （j） ρ
ρ < j=2,3,…,n
(b) ∂ ≥ ρ（j）≥ 0, j=2,3,…,n
(c ) ∂ < 0.5
g n i s s e c o r p n o i t a r e n e g y a r g , p e t s d r i h t e h T
s i g n i s s e c o r p y a r g e m o s , e r o f e r e h T . r a l u g e r r i e b y a m m e t s y s e h t n i a t a d l a n i g i r o e h t f o e g n a h c e h T
d e d e e
n to reduce therandomnessoftheorigina ldata sequence ,and theorigina ldatasequenceis :
n i a t b o o t e c n o d e t a l u m u c c a
{
(1) (1) (1) (1)}
) 1
( P (1),P (2),...P (i)...P (n)
P = ( 5)
e r e h
W :
) 0 ( )
1 (
1 （k） n (k)
k
P P
=
= ∑ ( 6)
) 1 (
Z iscalledt hei mmediateneighboroft hegeneratesequenceof P(1), i e
{
(1) (1) (1) (1)}
) 1
( Z (1),Z (2),...Z (i)...Z (n)
Z = ( 7)
e r e h
W :
) 1 ( )
1 ( )
1
(（k） 0.5(P (k) P (k))
Z = + )( 8
: l e d o m ) 1 , 1 ( M G e h t d e l l a c s i n o it a u q e l a i t n e r e f f i d y a r g g n i w o ll o f e h T
) 1 ( )
0
( (k) Z (k)
P + α = µ ( 9)
t s r i f e h t , p e t s h t r u o f e h
T -orderalbinismdifferentia lequationwasestablishedfor P(1) toobtain:
) 1 (
) 1 ( p
d P
t
d +α =µ (10)
, m e h t g n o m
A α iscalledt hedevelopmen tfactor , µ isknownast heamoun tofgray. e
v l o s o t , p e t s h t f i f e h
T α and µ basedont heprincipleofl eas tsquares:
e m u s s
A cˆ α
µ =
�and 1
1 . .
. .
1 . .
1
) 1 (
) 1 (
) 1 (
) 1 (
) 2 (
) 3 (
) i (
) n (
Z Z
A
Z
Z
−
−
=
−
−
. . .
) 0 (
) 0 (
) 0 (
) 0 (
) 2 (
) 3 (
) i (
) n (
N
P P
Y
P
P
=
, then:
= ( )1
ˆ T T
N
Y A A A
c = αµ −
) 1 1 (
t s r i f e h t o t n o i t u l o s e h t , e r o f e r e h
T -orderalbinoequation
) 1 (
) 1 (
p
d P
t
d + α = µi : s
) 0 ( )
1 (
ˆ（k+1） (P (1) )e k
P µ α µ
α α
− +
−
= (12)
: y r e v o c e r a t a d , p e t s h t x i s e h T
e h t , g n i t a l u m u c c a y b d e t a r e n e g s i l e d o m ) 1 , 1 ( M G y a r g f o m s i n a h c e m n o i t c i d e r p e h t e c n i S
n i e b o t d e e n s t l u s e r d e t a r e n e g e h T . s n o i t i d d a h c u s n o d e s a b s i e u l a v d e t c i d e r
p verselygeneratedand
n o i s i c e d n i d e s u e b n a c s t l u s e r e h t e r o f e b d e r o t s e
r -making .Based on theprediciton mechanism of
d e t a r e n e g e h t e c u d e r d n a e t a r e n e g y l e s r e v n i o t y r a s s e c e n s i t i ,l e d o m ) 1 , 1 ( M G y a r
g Pˆ(（1) k+1）
d n a , o
t n r u t e
r Pˆ(0（) k+1） .Thespecificprocessi s
: s i e c n e u q e s d e t c i d e r p e h T
{
(1) (1) (1) (1)}
) 1 (
ˆ Pˆ (1),Pˆ (2),...Pˆ (i)...Pˆ (n)
P = (13)
n i g n i t l u s e r , d e t c a r t b u s y l e v i t a l u m u c s i e c n e u q e s e v o b a e h
T :
{
(0) (0) (0) (0)}
) 0 (
ˆ Pˆ (1),Pˆ (2),...Pˆ (i)...Pˆ (n)
P = (14)
n e h
T Pˆ(0) canbeusedast hebasisfort hedecision-makingprediciton ,ofwhich:
) 1 ( )
1 ( )
0 (
ˆ（k） Pˆ（k） Pˆ（k-1）
P = − (15)
c i d e r p ) 1 , 1 ( M G y a r g e h t f o t r a h c w o l f g n il e d o m e h t , y r a m m u s n
I tionmodeli sshowni nfigure1 .
o l F . 1 e r u g i
F wchar tofGreypredictionmodel.
l a t n e m i r e p x
E Resutls
. 2 e r u g i
F Consistencyofpredictedvalue.
t , e v o b a 2 e r u g i F n i n w o h s s
A heexperimenta lresultsshowt ha tthegraymodeli susedt opredic t e
c i r p d l o g e h
t inadvanceandt hedatafrom2007t o2015arei ntroducedt ot hemodelt ostudy .Then a e v a h 6 1 0 2 o t 5 1 0 2 m o r f d e t c i d e r p e b o t e u l a v l a e r e h t d n a e u l a v d e t a l u m i s e h t , d n e r t e c i r p d l o g e h t
t a d l a i c n a n i f f o t s a c e r o f e h t n i , o i t a r a h c u s , % 4 . 1 8 f o y c n e t s i s n o
c a ,hasaverysignifican tpersuasion.
e t u l o s b a n a e m ( E P A M y b d e r u s a e m e b n a c l e d o m y a r G g n i s u s n o i t c i d e r p r u o f o y c a r u c c a e h T
e r e h w ) r o r r e t n e c r e
p :
𝐸 𝑃 𝐴
𝑀 � 𝑛1∑ |𝑖𝑛=1 𝑥𝑖� 𝑦𝑖|� 100% ( 61 )
a l u m r o f e v o b a e h t n
I :𝑥𝑖demonstratesthetruevaluewhile 𝑦𝑖 demonstratesthe predicted value T
. l e d o m r u o m o r
f hesmallert hevalueofMAPE,t hehighert hepredictionaccuracy .Theresutlsare w
o l e b 3 e l b a T n i n w o h
. e u l a v e u r t d n a e u l a v d e t c i d e r p n e e w t e b E P A M . 3 e l b a T e
t a D
h t n o
m Truevalue/(d𝑡o^ll(a− )r1 ) ・ Predictedval・ue/𝑡(^d(o− )l1 ) lar Relativeerror 5
1 0 2 . n a
J 1,278.50 1,236.40 3.29%
5 1 0 2 . b e
F 1,212.60 1,143.50 5.70%
5 1 0 2 . h c r a
M 1,183.10 1,243.30 5.09%
5 1 0 2 .l i r p
A 1,182.40 1,120.20 5.26%
5 1 0 2 . y a
M 1,189.40 1,204.90 1.30%
5 1 0 2 . e n u
J 1,171.50 1,145.50 2.22%
5 1 0 2 . y l u
J 1,094.90 1,131.30 3.32%
5 1 0 2 . g u
A 1,131.60 1,096.40 3.11%
p e
S - 51 1,115.50 1,193. 30 6.97%
5 1 0 2 .t c
O 1,141.50 1,081.30 5.27%
5 1 0 2 . v o
N 1,065.80 1,037.70 2.64%
5 1 0 2 . c e
D 1,060.30 1,121.90 5.81%
% 1 3 . 4 s i e u l a v E P A M e h t d n a % 6 n a h t s s e l s i h t n o m h c a e f o r o r r e t s a c e r o f e h t t a h t d n u o f e b n a c t I
e h t f o y c a r u c c a g n i t s a c e r o f e h T . s h t n o m 2 1 n
i graymodeli sbetter ,whichcanbettergraspt heessence
l e d o m e h t , e c n e h , e g n a h c e c i r p d l o g e h t f
o -based forecasting mode lcan be applied to predic tthe
. s t n e m e v o m e c i r p d l o g l a n o i t a n r e t n i
s n o is u l c n o C
a f t c a p m i d l o g e h t t c i d e r p o t d o h t e m a , r e p a p s i h t n
I ctorand priceforecas tusing machinelearning
t e l , s c i m o n o c e d n a s c i t s i t a t s l a n o i t i d a r t f o s e l k c a h s e h t f o d i r t e g o t g n i y r T . d e s o p o r p s a w s d o h t e m
y a r g d n a i r o i r p A m h t i r o g l a e l u r n o i t a i c o s s a e h t h g u o r h t y l n i a m , h c t a r c s m o r f s t r a t s e n i h c a m e h t
m n o it c i d e r
p odel ,taking the aging factor into the mode lfor more accurateprediction of the gold f o n o i s i c e r p a g n i v e i h c a f o t c e f f e e l b a k r a m e r a d e d i v o r p l e d o m r u o t a h t d e w o h s s t n e m i r e p x E . e c i r p
, % 5 w o l e b E P A M h t i w , % 4 . 1
8 whichwasveryexciting.
w , t n e m i r e p x e e h t n
I e stil lfound some areas for improvement ,and we had the following n o i t a z i m i t p o r e h t r u f g n i w o l l o f e h t d e d u l c n i e s e h T . s l e d o m e s e h t f o s e i c n e i c i f e d e h t r o f s n o i t a z i m i t p o
p m o c s t i e v o r p m i o t m h t i r o g l a i r o i r p A f o n o i t a t u p m o c e h t n o s n o i t c i r t s e r e r o m g n i d d a y
b utaitona l
d a h k r o w t e n l a r u e n e h t ,l e d o m k r o w t e n l a r u e n e h t o t n i l e d o m n o it c i d e r p y a r g r u o g n i d d e b m e ; y t i l i b a
f l e s f o y ti l i b a e h
t -learning ,nonilnearmappingand paralle ldistributedprocessing .Theideaofgray o
c y l e v i t c e f f e e r e w k r o w t e n l a r u e n d n a m e t s y
s mbinedt obuildgrayneura lnetworkmodel ,whichcan
s a o s , g n i p p a m r a e n i l b u s k r o w t e n l a r u e n d n a g n i l e d o m a t a d y a r g f o s e g a t n a v d a e h t o t y a l p l l u f e v i g
d o h t e m e h t t a h t n i a g a e c n o e z i s a h p m e o t t n a w e w , y l l a n i F . y c a r u c c a n o i t c i d e r p e h t e v o r p m i r e h t r u f o t
e
w provideis relatively easyto learn and to develop .Wehopetha tmoreideasand improvements y a l p n a c d n a s l e d o m n o i t c i d e r p r e g r a l o t n i d e d d e b m e , e r u t u f e h t n i l e d o m r u o o t n i d e c u d o r t n i e b l l i w
. n o i t c i d e r p f o d l e i f e h t n i e l o r t n a t r o p m i e r o m a
s e c n e r e f e R
[ ]1 Tschoeg lA.E .Efficiencyi nt hegoldmarke t— anote☆[J] .Journa lofBanking&Finance ,1980 , )
4 (
4 pp . 13 - .7 7 9 ]
2
[ Fama E.F. ,French K.R .Business Cycles and the Behavior of Metals Prices [J] .Journa lof p
p ) 5 ( 3 4 , 8 8 9 1 , e c n a n i
F . 1075- .9 3
] 3
[ SjaastadL.A .Thepriceofgoldandtheexchangerates :Onceagain J[ ] .EconomicsDiscussion , p
p ) 2 ( 3 3 , 7 0 0
2 . 81 - .1 2 4
] 4
[ Ede lTully ,Brian M .Lucey .ApowerGARCHexaminationoft hegoldmarket[J] .Researchi n 6
1 3 p p ) 2 ( 1 2 , 7 0 0 2 , e c n a n i F d n a s s e n i s u B l a n o i t a n r e t n
I - .2 5
] 5
[ Pitigalaarachch iA. ,Jayasundara D.D.M. ,Chandrasekara N.V .Modeling and Forecasting Sr i s
e c i r P d l o G n a k n a
] 6
[ TripathyN .ForecasitngGoldPricewithAutoRegressiveIntegratedMovingAverageModel[J] . p
, 7 1 0 2 , s e u s s I l a i c n a n i F & s c i m o n o c E f o l a n r u o J l a n o i t a n r e t n
I . .7
] 7
[ YangXinyuan ,ZhaoYingqi ,Wuman ,e tal .Themathematica lmode loft hepricepredictionof .
] J [ s l a t e m s u o i c e r
p ModernEconomicInformation ,2016(8)pp104-06 . ]
8
[ Gu Lemin . Analysis of GM grey mode l and the theory [J] . computer engineering and ,
s n o it a c i l p p
a 2016 ,52(6)pp. 85 - .6 3 ]
9
[ Zhang Junli ,Song Jiayou ,Yao Miao ,e tal .Improving ATS tes tinstrumen tcalibration cycle .
] J [ l e d o m y e r g n o d e s a
