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2550
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: : . : 2550
SVR (Support Vector Regression) ANFIS (Adaptive Neuro-fuzzy Inference System)
SVR (Global Optimization) OCB (Output Component Base)
ANFIS OII (Output-Input-Iteration)
NFSV (Neuro-Fuzzy with Support Vector Guideline System) OCB OII
( 177 ) :
Name : Mr.Tong Srikhacha Thesis Title : Short-Term Prediction in Stock Price Using Hybrid Optimized
Recursive Slope Filtering, Adaptive Moving Approach and Neurofuzzy Adaptive Learning
Major Field : Information Technology King Mongkut's Institute of Technology North Bangkok Thesis Advisor : Assistant Professor Dr.Phayung Meesad Academic Year : 2007
Abstract The objective of this research focuses on developing an intelligent stock
prediction based on historical data. The proposed system is applied by combining advantages of the principle of support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS).
The SVR is based global optimization of data classification. This method is benefit to apply in approximate prediction. However, it may provide poor output in case of inappropriate input offset. To reduce this effect, this research develops the output component base model (OCB).
The ANFIS is a type of fuzzy that can quickly adaptively learn, based on appropriate fuzzy rule defining. Because of its local optimized design, it may result in the surface oscillation output. To reduce this effect, this research develops output-input-iteration model (OII).
The Neuro-Fuzzy with Support Vector guideline system (NFSV) is synergistically combined these two techniques with stock rule filtering and suitable input reforming. In this research, NFSV model provides good stock prediction especially in lowest and opening price.
(Total 177 pages)
Keywords : Stock Prediction, Neuro-Fuzzy, Support Vector, Out Component Base,
Output-Input-Iteration
Advisor
. ()
1 1
1.1 1 1.2 2 1.3 2 1.4 2 1.5 3 1.6 4
2 5 2.1 5 2.2 7 2.3 12
2.3.1 12 2.3.2 13 2.3.3 13 2.3.4 AMA 14 2.3.5 15
2.4 17 2.4.1 18 2.4.2 SVR 26
2.5 ANFIS 29 2.5.1 31 2.5.2 ANFIS 32 2.5.3 ANFIS 34
()
2.6 43 2.6.1 AMA 43 2.6.2 SVR 46 2.6.3 ANFIS 54
2.7 60 3 61
3.1 61 3.2 62 3.3 62 3.4 63 3.5 65
4 67 4.1 67
4.1.1 67 4.1.2 70
4.2 72 4.3 73
4.3.1 OCB 73 4.3.2 OII 80
4.4 NFSV 84 4.4.1 86 4.4.2
92 4.4.3 96 4.4.4 97 4.4.5 101 4.4.6 105
4.5 NFSV 113
()
5 115 5.1 115
5.1.1 AMA 115 5.1.2 SVR OCB 121 5.1.3 ANFIS OII 126
5.2 1 134 5.2.1 134 5.2.2 (5-11) 136 5.2.3 140
5.3 NFSV 144 5.4 155
6 157 6.1 157 6.2 ANFIS 157 6.3 NFSV 158 6.4 158 6.5 159
163 169
170 177
4-1 9 69 5-1 AMA 120 5-2 AMA 1 2 121 5-3 5-8 123 5-4 5-9 124 5-5 Ustat SET 133 5-6 Ustat 5-16 5-19 139 5-7 SET TMB IRP 139 5-8 Ustat 5-21 143 5-9 143 5-10 143 5-11 NFSV 10 145 5-12 NFSV OCB 146 5-13 NFSV OCB 147 5-14 IRP 149 -1 170
2-1 13 2-2 14 2-3 15 2-4 17 2-5 18 2-6 19 2-7 H1 H2 20 2-8 20 2-9 21 2-10 SVR 27 2-11 28 2-12 SVR 29 2-13 30 2-14 31 2-15 ANFIS 32 2-16 37 2-17 38 2-18 39 2-19 40 2-20 ANFIS 40 2-21 AMA 1 44 2-22 AMA 2 45 2-23 45 2-24 SVR C 47 2-25 C 48 2-26 48 2-27 u v 49
()
2-28 SVR 50 2-29 SVR 50 2-30 SVR 51 2-31 52 2-32 SVR y = - sin(t) x = sin(t) 52 2-33 y = - sin(t) x = sin(t) 52 2-34 x 2-32 53 2-35 SVR y = cos(t) x = sin(t) 53 2-36 x 2-35 53 2-37 y = cos(t) x = sin(t) 54 2-38 ANFIS 55 2-39 radii = 0.07 56 2-40 57 2-41 radii 58 4-1 9 68 4-2 4 SET KEST 69 4-3 OCB 76 4-4 SVR( ) 76 4-5 SUB( ) 77 4-6 SUB( ) 78 4-7 WIN( ) 79 4-8 SVR OCB 80 4-9 NFSV 84 4-10 SET Index of Thailand 87 4-11 88 4-12 89 4-13 89
()
4-14 NFSV 90 4-15 Tj (4-23) 94 4-16 NFSV 97 4-17 98 4-18 radii 100 4-19 NFSV 102 4-20 106 4-21 107 4-22 105 4-23 108 4-24 109 5-1 AMA 116 5-2 10 AMA 117 5-3 117 5-4 AMA 1 118 5-5 AMA 2 118 5-6 AMA 1 2 119 5-7 AMA 1 2 119 5-8 SVR OCB SET (5-1)
REC 123 5-9 SVR OCB SET (5-2)
REC 124 5-10 SET (5-2)
m=1.5 125 5-11 SVR OCB Mackey Glass time series 126 5-12 126 5-13 ANFIS OII 130
()
5-14 MF Rule SET 131 5-15 RB MF Rules 133 5-16 1 135 5-17 SET m 137 5-18 TMB m 138 5-19 IRP m 138 5-20 (4-5) (4-7) m 140 5-21 8 Ustat 141 5-22 IRP 148 5-23 REC IRP 148 5-24 IRP 149 5-25 BRG IRP 150 5-26 NFSV IRP 151 5-27 NFSV IRP 152 5-28 NFSV SET 153 5-29 NFSV SET 154 -1 REC 172 -2 BRG 173 -3 BRG 171
1
1.1
[1] [2]
(Neural Network) Yao [3] DNN Kung [4] SML Hellstrm [5] (Rank Measurement) (Mutual Funds) Hellstrm
2
1.2
(Neuro-Fuzzy) (Support Vector)
1.3
Nave Exponential Smoothing
1.4
Adaptive Learning Adaptive Moving Approach
(Movement) (Trends) (Season)
Exponential Smoothing (Moving Average) (Time Series)
Nave Model
Neuro-Fuzzy (Neural Network) (Fuzzy Logic) - (If-Then)
Recursive Slope Filtering
Support Vector (Classification) (Minimized Empirical Error) (Maximized Margin)
3
1.5
(Support Vector) (Neuro-Fuzzy) (Experimental Research)
1.5.1
(Support Vector Regression) (Neuro-Fuzzy System)
(Batch Processing)
1.5.2 10
2 1 SET index 5 BBL (Bangkok Bank
Public Co.) KTB (Krung Thai Bank Public) SCB (The Siam Commercial Bank) TISCO (Tisco Bank Public Co., Ltd.) TMB (TMB Bank Public Co., Ltd.) 4 IRP (Indorama Polymers Public) PTTCH (PTT Chemical Public) TPC (Thai Plastic And Chemical) VNT (Vinythai Public Co., Ltd.)
3 2549 28 2550 3 2549 28 2549 1 2549 28 2550 2 (Recursive Slope Filtering)
1.5.3 2
Nave
4
1.6
2
3
AMA (Adaptive Moving Approach) [29] SVR (Support Vector Regression) [30, 31, 32] ANFIS (Adaptive Neuro-Fuzzy Inference System) [24, 25]
AMA (Movement) (Trends) (Season) SVR ANFIS 2.3 2.4 2.5 3 4
2.1
30 [6] (Movement) (Trends) (Season)
(Exponential Smoothing) [7] (Double Exponential Smoothing) [8] (Non-Stationary) [9]
6
ARIMA model (Autoregressive Integrated Moving Average) Box Jenkins ..1970 [10] [11] [6], [8], [12] (Markov Model) [11]
(Adaptive Approach) ARIMA [13]
Bayes [14]
(Genetic Algorithm) [15]
(Genetic Algorithm) (Random Walk Time Series) [16, 17, 18] (Correlation) [19] (Clustering) [20] [21, 22] (Cosine Radial Function)
(Neural Network) (Fuzzy)
7
[8] [23] ANFIS (Adaptive Neuro-Fuzzy Inference System) [24, 25]
ANFIS (Adaptive Neuro-Fuzzy Inference System) (Cosine Distance) [17], [26, 27, 28] (Adaptive Learning Approach) (Noise Optimization )
AMA ANFIS SVR
2.2
2.1 Parametric Non-Parametric Model ARIMA [13]
8
Bayes [14]
(Stock) (Common Stock) (Preferred Stock) (Debenture) (Convertible Debenture) (Warrant) (Short-Term Warrant) (Derivative Warrant) (Unit Trust)
80% [60]
P SCB-P, TISCO -P
(Par Value) 10-12% [61]
9
2 (Fundamental Analysis) (Technical Analysis)
(Fundamental Analysis)
(Technical Analysis)
3 (SET Index) SET50 Index SET100 Index (Industry Group Index) (Sectoral Index) (Price) (P/E Ratio) (Dividend Value) (Volume) (Value)
(SET Index) SET Index ( 1 ) 30 2518
10
SET Index SET50 Index SET 100 Index 50 100 16 2538 (SET50) 30 2548 (SET100) SET50 Index SET100 Index 6 ( )
(Industry Group Index) (Sectoral Index) 8 25
(Price ) -
11
GDP (Gross Domestic Product) GDP
(Fundamental Analysis) (Technical Analysis) MA (Moving Average) MACD (Moving Average Convergence Divergence) Bollinger Bands ADX (Directional Movement Index) RSI (Relative Strength Index)
(Exponential Smoothing) (Fuzzy) (Support Vector)
AMA ANFIS SVR 3 4
12
2.3 (Adaptive Moving Approach) AMA
3
(Re-Testing) AMA 4 (Movement) (Trend) AMA
2.3.1 (Movement) (Smoothing)
ARRSES (Adaptive Response Rate Single Exponential Smoothing) [37] ARRSES (2-1)
ttt FYF )1()ARR(
1 +=+ (2-1) tF
tY )ARR( 1+tF 10 (2-1)
1 Nave 0
13
2.3.2 (Trend)
2-1 (Period) (2-2)
2-1
( ) ( ) 11 1 += tttt GSSG (2-2)
( )1 tt SS 1tG
2.3.3 (Season) 5
- (Seasonal Factor) - (Seasonal Value) - AMA (Adaptive Moving Approach) - -
2.3.3.1 tt SD / (2-3)
( ) ( ) N,1/ tttt CSDC ++= (2-3)
tt SD /
N,tC tC (Last
14
Period Seasonal Factor) N 1N >
2.3.3.2 2-2
2-2
tS (Seasonal Value)
N,tC tD (Actual Value) (2-4) (2-5)
ttt DSC N, (2-4)
( ) ( ) ( )11, 1/ ++= ttNttt GSCDS (2-5) tG (One-Period Trend Estimate)
2.3.4 AMA (Adaptive Moving Approach) (2-2) (2-5) (2-6)
( ) N,1)AMA( 1 ++ += tttt CGSF (2-6)
N (Auto Correlation Function: ACF) AMA N 4 N,tC
15
2 1 (Type-I) 2 (Type-II) (2-7) (2-8)
Type I: ( ) 1N1N,1 SDN1
=+ = iittt CCC (2-7)
Type II: ( ) ( ) 1N1N1N1N,1 SD
=+++ += iittttt CCCCC (2-8)
SD (Standard Deviation)
1 2 SD
2.3.5 AMA
2 AMA
2.3.5.1 2-3
2-3
(2-9) (2-13)
( ) 11 += ttt AEA (2-9)
( ) 11 += ttt MEM (2-10)
1
1
=t
tt M
A (2-11)
16
( )( )ttttt E +=+ 1sgn1 (2-12)
( )( )ttttt E +=+ 1sgn21
1 (2-13)
tA (Smoothing Error) tM (Absolute Smoothing Error) ttt FDE = 1,,0 10
17
SVR
2.4 (Support Vector Regression)
SVR (Pattern) (Historical Space: H-Space) (Target Space: T-Space) 2-4
2-4
(Over Fitting) SVR
gene (2-14) 2-5
estappgen eee += (2-14)
1h 2h jhTarget space
jhhh
18
2-5
este (Estimate
Error) appe (Approximate Error)
SVR
2.4.1 (Minimized Empirical Error
or Risk) (Maximized Margin)
SVR SRM (Structure Risk Minimization) ERM (Empirical Risk Minimization)
)(xG (Unknown Function) [ ]lT III ,...,, 21=x l y (Family Function) (2-15) n
( ) ( ) ( ){ } lnn yxyxyx ,,...,,,, 2211 (2-15)
y 3
(Binary Classification) (Scalar Regression) (Multiclass Classification) 1 3
gene
appe
este
h space
Target space
19
(Image Recognition) OCR 2 (Prediction Application)
(2-16) w (Weighting) ( ) x )(G (2-16) ( ) w
( )= xwx iiwf ),( (2-16)
SRM (Feature-Space or High Dimension Space) (2-17) 2-6
bf += xwwx T),( (2-17)
b (Bias) T (Matrix Transpose)
2-6
y (2-17) )(xG
)(xF
G plane F plane
20
)(xF w b y 1 2 -1 +1 (2-17) (2-18) (2-19)
1;1
1;1
=+
+=++
iiT
iiT
yb
yb
xw
xw (2-18)
( ) iby iTi + ;01xw (2-19)
2 b w 2-7 (Margin) (Hyperplane) H1 H2
2-7 H1 H2
2-8 n ( )npppp xxx ,,...,, 21=x
2-8
M
2D
1D
12
1x 2x
1H Margin
w
2H
Origin
wb
F plane
21
1x 2x 1=+ by jTj xw ( ) 0,, =bd wx
D (2-20) 1 2 (2-21) (2-22)
222
21
2211
...
...
n
npnppp
www
bxwxwxwbD
+++
++++=
+=
w
wx (2-20)
( )wxxw
x
T
1
1
1
11cos ==
D (2-21)
( )wxxw 2
T
22cos = (2-22)
w2
21 == DDM (2-23)
(Soft Margin) i (Slack Variable) li ,...,1= (2-24) 2-9
1;1
1;1
=++
+=++
iiiT
iiiT
yb
yb
xw
xw (2-24)
ii ;0
2-9
F plane
*
F Margin
w
*
*
0
22
w ),( wxF (Mapping Function) 2 (Quadratic Function) ( -insensitivity)
(2-25) z (2-17) bT += zwwxF ),(1 (2-25)
(2-17)
+
==
1
11
dp
di
idCq ( )!!
!knk
nC nk = p (Order Polynomial)
d
[ ]1,,...,,,...,,...,,,...,)( 11212211 dddjid xxxxxxxxxx =xF (2-25)
Vapnik [33] (2-26) SVR
( )( ) bpn
i
++= =1
2 ),( 1xx-wxFT* (2-26)
*, ii Largrange Multiplier Pairs 1F 2F
1F q 2F 12 +n bii ,,
*
w R SRM 2 (2-27)
( )[ ]
+=
=
l
iii fyLCR
1
2 ,21 wxw (2-27)
(Margin) (Empirical Error) L (Loss Function) [ ] [ ]2=L C y
( )f
23
C R
w (Linear Algebra) (2-28)
[ ]wEVVyV T +=T (2-28)
V (Square Error) [ ]qn E (Diagonal Matrix) C/1 y (Dependent Variable Column Vector) [ ]1n
(2-29) 2-9
( )( )
( ) ,0, {
=
wx,wx,wx
fyifotherwisefyfy
(2-29) y ( -
Tube) C *, (Approximate Value) w (2-27) (2-30)
++=
==
l
i
l
i
CR1
*
1
2* 2
1,, ww (2-30)
(Lagrangian)
(Primal Objective Function) (Dual Sets) (Constrained Optimization) (2-31)
( )
[ ]
[ ] ( )
==
===
++++
++
++
=
l
iiiii
l
iiii
l
iiii
l
ii
l
ii
p
yb
by
bL
1
**
1
1
**
1
*
1
***
21
,,,,,,
i
i2
xw,
xw,Cw
w
(2-31)
24
( ) ( ) 0, ** (Lagrangian Multiplier Pairs) ixw,
iT xw
(Derivative) (2-31) bw ( )* 0 (2-32) (2-33)
( )=
==l
iiib L
1
* 0 (2-32)
( )=
==l
iiiiw xwL
1
* 0 (2-33)
0(*)(*)
(*)==
CL (2-34)
(Dual Optimization
Problem Solving) (Dual Space Karush-Kuhn-Tucker) (Minimize Regression) (2-32) (2-34) (2-31) (2-35)
( ) ( ) ( )
( )( ) jil
jijiii
i
l
iii
l
iiid
xx
yL
,21
1,
**
1
*
1
**
=
==
+++=
, (2-35)
==
=l
ii
l
ii
11
* Cii *,0 li ,..,1=
(2-35) (2-36) (Quadratic Optimization Problem)
( ) TTd QL c+= 21 (2-36)
=
HHHH
Q Hessian [ ]1+= xxH T * =
[ ]NNT yyyyyy +++= ,...,,,,...,, 2121c
25
(2-35) (*) (2-36) ( )
=
=l
iiii xw
1
* (2-37)
( ) ( ) bxxxf iii += ,* (2-37) (2-17) (2-38)
(Nonlinear Kernel Function) (2-39) (Gaussian Radial Basis Function)
( ) bf T += xzw (2-38)
( )
= 2
2
2exp,
i
i
xxxxk (2-39)
( )z
(2-40) SVR
( ) ( ) bfz +== iT xx,kwwx, (2-40) (2-38) (Optimized Weighting)
(2-41)
w *T ==o (2-41) b
(Dual Variable) 0 (2-42) (2-43)
( )( ) 0,
0,** =++
=+++
bxwy
bxwy
iiii
iiii
(2-42)
26
( ) 0(*)(*) = iiC (2-43) 2 (Elbow
-Tube) C=(*) 0(*) = (2-44) (2-45)
Cifbxwy iiiii ++ iiii ifbxwy (2-45)
b (2-46) (2-47)
( )( )
=
=
l
iio yl
b1
1ixx,kw (2-46)
( ) ( ){ } += i kkiiikk xxkyaverageb * (2-47)
( )*sgn iik =
SVR 2 2.4.2
2.4.2 SVR 2.4.2.1
SVR (Weighting) trnX [ ]ln, n (Training Records) l
C () (Support Vector: SV)
27
SVR 2-10 (Mapped Vector) (Dot Product) -
(Kernel Function) Polynomial Function Gaussian Radial Basic Function Exponential Radial Basic Function Multi-Layer Perception Function Spline Function B-Spline Function
2-10 SVR
() u v l
(Euclidean Product) (Gaussian Radial Basic Function) (2-48)
( )( )
22, vu
evuk
= (2-48)
1
SV
( . )trnX
trnX1 trnX2
trnXn
1u
( . )2u
( . )nu
1v
2v
nv
)(
)(
)(
)(
)(
)(
Mapped vector
Dot product
Weight Output
2
n
28
u v 2-11 (.)
u v i iu iv
iju ijv
2-11
(SV) trnX SVR SVR
2.4.2.2 SVR
trnX 2-12
tstX trnX iv
29
2-12 SVR
u v
(Euclidean Distance) tstX
ANFIS
2.5 ANFIS (Adaptive Neuro-Fuzzy Inference System)
ANFIS ANFIS
(Crisp Logic) - (if-then) (Fuzzy Logic)
4 (Close) (High) (Low) (Open) 2-13
1 ( . )
( . )2u
( . )nu
1v
)(
)(
)(
)(
Mapped vector
Dot product
Weight Output
2
n
trnX trnX1
trnX2
trnXn
tstX tstX
30
4 - R 1 2 3
2-13
2-13 (Back Propagation)
(Mandani Fuzzy)
(Sugeno Fuzzy)
C
H
L
O
Rule 1:
Rule 2:
Rule (R-1):
Rule R:
Up
Down to Up
Down
Up to Down
Input Rule Output Trend
C H
O L
O H
C L
C: Close H: High L: Low O: Open
31
(Output Membership Function)
ANFIS 2.5.1 ANFIS 2.5.2 2.5.3 ANFIS
2.5.1 (Sugeno Fuzzy) (TS: Takagi-Sugeno Fuzzy)
2-14 2 (Dimension) (Input MF) (Rule Weight Firing Strength) (2-49)
2-14
=
== n
ii
n
iii
out
w
ZwY
1
1 (2-49)
n w (Gaussian Function)
1x
2x
Input MF
cbxaxz ++= 21
w Rule Weight
ANDInput MF
Output MF
32
(Output MF) 2 (Zero Order) 0== ba (First Order) cbxaxz ++= 21 ANFIS ANFIS
2.5.2 ANFIS
5 2-15 (2-49) ANFIS (2-50)
(2-55)
2-15 ANFIS
1x
2x
Input MF
cbxaxz ++= 21
wRule Weight
AND
Input MF
Output MF
A1
Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6
A2
B1
B2
N1 11
N2 22
N3 33
N4 44
=
== n
ii
n
iii
out
w
ZwY
1
1
1x 2x
2x
1x
w
33
1 x n (2-50) 2-15 n 2
[ ]= ,,,2,1 ,,,,, ni xxxx LLx (2-50)
ni 1 2 (Fuzzification Layer)
(2-51)
22)(
,,
,,
ji
cjii
x
ey ji
= (2-51)
( ) 2 ni 1 R1 j n R c (Subtractive Clustering Function) 2.5.3.1.
3 (Fuzzy Rule Layer) 2 (2-52) w (Ru) 3
( ))(,1)Ru(
jiijyy
n
== (2-52)
4 (Normalization Layer) (2-53)
( ) 4
=
= R
1
)Ru(
)Ru()(
jj
jj
y
yy (2-53)
5 (Defuzzification Layer) (2-54)
34
( )
+=
=+
n
nji
iijjj kxkyy1
)1,,)()Df(
(, (2-54)
(Df) 5 k (Moore-Penrose Pseudo Inverse of a Matrix) 2.5.3.1. k ( )[ ]1R + n
6 (Summation Neuron) ANFIS (2-55) (2-49)
{ }( ) ==R
)Df()TS(,,,TSj
jyyckx (2-55)
{ }( ) ,,,TS ckx x { },,ck
TS ANFIS 4
(2-50) (2-55) ANFIS [ ] nxi :, [ ]R:)(, ny ji [ ]R1:)Ru( jy [ ]R1:)( jy
[ ]R1:)Df( jy [ ]R:, nc ji [ ]R:, nji [ ])1(R:, + nk rj ANFIS
2.5.3 ANFIS 2
2.5.3.1
)
2 (Un-Supervised Learning) (Supervised Learning)
35
(Weighting) (Hidden Node) (Black-Box Model) (White-Box Model) - (If-Then Rules)
(Data Clustering)
2-15 2 (Logic Part Fuzzy Antecedent Part) (Mathematic Part Fuzzy Consequent Part)
- If Input1 = x and Input2 = y Then z = ax+by+c a b c (Output Membership Function or Output MF) Input1 Input2 (Input Membership Function or Input MF)
3 (Grid Partitioning) (Tree Partitioning) (Scatter Partitioning)
36
(Domain) km m k (Input Dimension)
2 (Hard-Clustering) (Soft-Clustering) - (K-Means) (FCM) (Mountain) (Subtractive Clustering)
- - (Hard C-means Clustering) (Euclidean Distance Function Minimize Cost Function) - (Fuzzy C-Means Clustering) - (Degree of Membership) (Overlapped Grouping) - (Clustering Centers)
(Mountain) (Subtractive Clustering) (Data Point Density
37
Center)
(Highest Density Value) (2-56)
=
=
n
ji
jxixeP1
2
(2-56)
2/4 ar= (2-57)
n x ar
ar (2-57) 2-16
2-16 (2-56)
1 (2-58)
0
0.2
0.4
0.6
0.8
1
1.2
-5
-4.4
-3.8
-3.2
-2.6 -2
-1.4
-0.8
-0.2 0.4 1
1.6
2.2
2.8
3.4 4
4.6
8/ar=
38
2*
* kxix
ePPP kii
(2-58)
*kP *kx (Potential Value) x 2/4 br= Chiu [34] ab rr 25.1=
ar
2-17 0.3 0.8 yx = (Noise) 1% 15
ar = 0.5 2 0.26 0.78 ar ar 1
0
0.2
0.4
0.6
0.8
1
1.2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1
Train data Center
2-17 2
2 x 2 c (2-59) (2-60)
*kk xc = (2-59)
39
125.188 == kkk rr (2-60) 2-18
0
0.2
0.4
0.6
0.8
1
1.2
0
0.05 0.1
0.15 0.2
0.25 0.3
0.35
0.41
0.45 0.5
0.55 0.6
0.65 0.7
0.75 0.8
0.85 0.9
0.95 1
Input-MF1 Input-MF2 data center
2-18
(Output-MF Consequent Parameters) k (2-54) (2-55) (2-61) (2-63)
( ) ( )n YXYA += (2-61)
( )outYB = (2-62)
B\AZK == (2-63)
( ) )TS(y=outY ( ) )(y=nY ( )Y (2-55) (2-53) (2-52) X
k (2-63) 2-19 ( )Dfy )TS(y
2-15 (2-52) k ANFIS
40
2-20
0
0.2
0.4
0.6
0.8
1
1.2
0
0.06
0.12
0.18
0.24 0.3
0.36
0.43
0.48
0.54 0.6
0.66
0.72
0.78
0.84 0.9
0.97
Output-MF1 Output-MF2 data center Fout
2-19
2-20 ANFIS
ar k
1 1
A1
X1
X2
Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6
A2
B1
B2
N1 11
N2 22
X1 X2
41
ANFIS
) ANFIS
2
(Forward Learning) (Backward Learning) (Least-Squares Estimation) (Gradient Descent Method)
(2-64) (2-55) (2-54)
( ) ( ) ( )
+= +
R
1,,,)TS(
j ijiijj
n
nkxkyy
(2-64)
n R ( )jy j k
(2-64) (2-65) A ( )[ ]1RP + n K ( )[ ]11R +n P
AK=)TS(y (2-65)
( )( ) ( )( ) ( )( )( )( ) ( )( ) ( )( )
( )( ) ( )( ) ( )( )
=
1,,,,,1,,,,1,,,,
1,,,,,1,,,,1,,,,1,,,,,1,,,,1,,,,
P,P,1RP,P,12P,P,2P,11
2,2,1R2,2,122,2,22,11
1,1,1R1,1,121,1,21,11
nnn
nnn
nnn
xxyxxyxxxy
xxyxxyxxxyxxyxxyxxxy
LLLL
MMM
LLLL
LLLL
A (2-66)
( ) ( ) ( )[ ]= +++ 1,R1,R1,21,21,12,11,1 ,,,,,,,,,, nnn kkkkkkk LLLLK (2-67)
42
(Moore-Penrose Pseudo Inverse) k (Minimum of Square Error) (2-68)
( ) yAAAK TT 1=new (2-68)
y k c
(Backward Learning) (Square Error) (2-69)
( )22
2)TS(2 yyeE
== (2-69) (Gradient Descent Method)
(Derivative Chain Rule) (2-70) (2-72)
( )( )
csty
yu
uu
ufu
fuy
ye
eEcstEcst
i
i
i
i
ii
ii
=
=
(2-70)
( ) ( )
( ) ( ) ( )32
)TS(
)TS(
1
11
cxuufyy
yy
uufyy
iii
iii
=
= (2-71)
( ) ( ) ( )2)TS( 1 cxuufyyc iii
= (2-72)
(Learning Rate) cst c (2-71) (2-72) [ ]( ) ( )( )
=++=
n
rniirrii kxkf
11,,,
y=
(2-53) y= (2-51) x c (2-73) (2-74)
(2-71) (2-72) E
43
cccnew = (2-73)
=new (2-74) ANFIS 2
k c k c
c (2-73) (2-74)
ANFIS
2.5.3.2 ANFIS
(2-55) { },,ck x
(2-55) { },,ck
3 AMA SVR ANFIS 2.6
2.6
4
2.6.1 AMA AMA
,,
44
2
2.6.1.1 AMA ,,
{219, 216, 218, 185, 154, 147, 124, 93, 127, 148, 161, 198, 236, 239, 221, 194, 161, 131, 110, 101, 131, 157, 189, 217} 1 2 2-21 2-22 1 3 2-21() (2-12) (2-13) 2-21() 2-21() 2-21()
2-21 AMA 1 1 2-21() 2-21() ( = 0.1
= 0.1 = 0.1 = 0.1) N2 2-21() ( = 0.99 = 1 =0.381 = 0.89)
2 N,1+tC 2-22() () 2-22()
30
80
130
180
230
280
330
380
430
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
30
80
130
180
230
280
330
380
430
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
30
80
130
180
230
280
330
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
() ()
()
45
2-22() () ( = 0.1 = 0.1 = 0.1 =0.1) 2-22() ( = 0.97 = 0.89 = 0.312 = 0.89)
2-22 AMA 2
2.6.1.2 AMA )10sin()3sin( xxxy = 1 2 2-23() ()
2-23() ( = 0.19 = 0.59 = 0.219 = 0.9) 2-23() ( = 0.9 = 0.9 = 0.9 = 0.9)
2-23
30
80
130
180
230
280
330
380
430
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
30
80
130
180
230
280
330
380
430
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
30
80
130
180
230
280
330
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
() ()
()
-25
-20
-15
-10
-5
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
-100-80-60-40-20
020406080
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Dt Ft
() ()
46
AMA 1 2 N2 2 1 1 1
2.6.2 SVR SVR
(Global Approximation)
C SVR 4
2.6.2.1 C
C SVR (2-31) (2-32) C (Optimizing) C xey = 2-24
2-24 svrout (Ub) (Lb) SV Ub Lb
47
C minmax yy C
2-24 SVR C svrout
(Linear Regression)
2-25 (Gaussian Radial Basis Function) C
C Ub Lb SVR SV
SVR with linear kernel,C=0, e=2
-50
510
1520
2530
3540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with linear kernel,C=100, e=2
-50
510
1520
2530
3540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with linear kernel,C=100, e>(ymax-ymin)/2
-50
510
1520
2530
3540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with linear kernel,C=100, e
48
2-25 C 2-26
2-26
SVR with non-linear kernel,C=0, e=2, sigma=1
-505
10152025
303540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with non-linear kernel,C=100, e=2, sigma=1
-505
10152025
303540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with non-linear kernel,C=100, e>(ymax-ymin)/2, sigma=1
-505
10152025
303540
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
actual valuesvr outSVUbLb
SVR with non-linear kernel,C=100, e
49
2-25() 1 SV
2.6.2.2 u v
trnX tstX ( ji trnXatstX = ) a jtrnX itstX
jtrnX itstX 2-27 vu = vu tstX
2-27 u v
u or v
k(u,
v)
u or v
k(u,
v)
Actual valuePredict value
vu = vu
50
SVR SVR
2.6.2.3 SV SV (Sinc Function) ( )
xxy
sin
= (Sine
Function) 2-28 2-29 C = 100 = 0.1 = 1
2-28 SVR
2-29 SVR
51
SVR SV Ub Lb 2-29 2-30 SVR
2-30 SVR
2.6.2.4 ( )xy cos= 2-30
x y C = 100 = 0.1 = 1
2-31 x y (Quadratic Programming Problem) SV ( SV 2-29 )
)sin(ty = )sin(tx = SVR x y 2-32 2-33
)sin(ty = x y 2-34 x
52
y 2-33
2-31 2-32 SVR )sin(ty = )sin(tx =
2-33 )sin();sin( txty ==
53
2-34 SVR
)sin();cos( txty == SVR 2-35 2-36
2-34 x 2-32
2-35 SVR )sin();cos( txty ==
2-36 x 2-35
54
2-37
2-37 )sin();cos( txty == C
SV
SVR
2.6.3 ANFIS AMA SVR
6
- (Output Surface Oscillation) - - - (Over Fitting) - -
55
2.6.3.1
=
=5.0;9.05.0;0
XX
Y
radii ANFIS 2-38
radii ( ar )
2-38 ANFIS radii
ANFIS
2-39 2-38 radii = 0.07 (Defuzzification)
-0.2
0
0.2
0.4
0.6
0.8
1
0.3
0.33
0.36
0.39
0.42
0.45
0.48
0.51
0.54
0.57 0.6
0.63
0.66
0.69
Train data radii=0.07 radii=0.1 radii=0.5
56
2-39 radii = 0.07
2.6.3.2
ANFIS
X = Y 0 1 x = 0.5 y = 0.2 0.9 = 0.0002 radii 2-40 x = 0.5 ANFIS x
radii radii 1
radii 1 ANFIS x radii x = 0.5
Defuzzify Output
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
0
0.06
0.12
0.18
0.24 0.3
0.36
0.42
0.48
0.54 0.6
0.66
0.72
0.78
0.84 0.9
0.96
57
(0.5 0.9) x (2-43)
0.2
0.3
0.4
0.5
0.6
0.7
0.80.
3
0.34
0.38
0.42
0.46 0.5
0.54
0.58
0.62
0.66 0.7
Yradii=0.1radii=0.5radii=0.9
2-40
2.6.3.3
SVR 2.5.2.4 )sin();cos( txty == ( )yx, x 2-36 ANFIS
ANFIS y ANFIS radii 2-41 radii = 0.28 0.15 0.1 10 19 31 30 50 86
SVR ANFIS
58
-1.5
-1
-0.5
0
0.5
1
1.5
1
0.97
0.91
0.81
0.68
0.56
0.49
0.33 0.2
0.12 -0.1
-0.3
-0.4
-0.6
-0.8
-0.9 -1 -1
Y radii=0.28 radii=0.15 radii=0.1
2-41 radii
2.6.3.4 (Overfitting)
2.6.3.5 ANFIS 2
59
(Optimized Linear Equation Problem)
(Gradient Descent Method) 2 c (2-60) (2-61)
c c (2-62) (2-63) (2-60) (2-61)
radii radii radii ANFIS 4.4.4
2.6.3.6 ANFIS
2
c
60
ANFIS
2.7
AMA
SVR C SVR (Global Approximation)
ANFIS ANFIS
4
3
(Neuro-Fuzzy) (Support Vector) (Experimental Research)
1. (Requirement Analysis) 2. (Data Preparation) 3. (Research Tool Development) 4. 5.
3.1 (Requirement Analysis)
(Fuzzy) [23]
(Minimized Empirical Error or Risk) (Maximized Margin)
62
(Global Optimizing)
(Neuro-Fuzzy) (Support Vector)
3.2 (Data Preparation)
10 2 1 SET index
5 BBL(BANGKOK BANK PUBLIC CO.) KTB (KRUNG THAI BANK PUBLIC) SCB (THE SIAM COMMERCIAL BANK) TISCO (TISCO BANK PUBLIC CO.,LTD.) TMB (TMB BANK PUBLIC CO.,LTD.)
4 IRP (INDORAMA POLYMERS PUBLIC) PTTCH (PTT CHEMICAL PUBLIC) TPC (THAI PLASTIC AND CHEMICAL) VNT (VINYTHAI PUBLIC CO.,LTD.)
3 2549 28 2550 3 2549 28 2549 1 2549 28 2550
3.3 (Research Tool Development)
NFSV (Neuro-Fuzzy with Support Vector Guideline System) (Neuro-Fuzzy) (Support Vector)
63
Nave AMA (Adaptive Moving Approach)
3 mse (Mean Square Error) [37] (U-Theil Ustat) [37] REC (Regression Error Characteristic) [42] [43]
Mse [37]
Ustat [37]
REC [42] [43] (Error Absolute Deviation) (Cumulative Accuracy) 1 REC
3.4 NFSV
NFSV 2 NFSV
3.4.1
(Exponential Smoothing) (Fuzzy) (Support Vector) 3 NFSV
AMA Exponential Smoothing NFSV
64
AMA 2 1 2 ( 2.3) NFSV mse Ustat 5.1.1
SVR (Support Vector Regression) [30, 31, 32] OCB (Output Component Based SVR) [35] NFSV mse Ustat REC 5.1.2
ANFIS (Adaptive Neuro-Fuzzy Inference System) [24, 25] OII (Output-Input-Iteration) [36] ANFIS OII (MF Rule /Training Data) 5.1.3
1 Ustat
MF Rule/ Training Data NFSV
3.4.2 NFSV NFSV
NFSV
NFSV Nave (Exponential Smoothing)
65
5.1.3.1 3 2549 28 2549 1 2549 28 2550 7
Ustat NFSV 4 ( )
AMA Ustat 1 Nave 1 Naive
5
3.5 mse Ustat REC
NFSV AMA ANFIS OCB OII SVR 3.3
mse Ustat REC
Ustat 1 Nave 1 Ustat 1
4
3 2
2
5 4.1 4.2 4.3 4.4 4.5
4.1
4 (Close: C) (High: H) (Low: L) (Open: O)
2 4
4.1.1
4-1 2543 2549 9 SET (Stock Exchange of Thailand) TPI (Thai Petrochemical Industrial) PLE (Power Line Engineering) ITD (Italian-Thai Development) KEST (Kim Eng Securities) BNT (BNT
68
Entertainment Public) TPC (Thai Plastic and Chemical) EWC (Eastern Wire Public Co.Ltd.)
EMC (EMC Public Co.Ltd.)
4-1 9
5
%C(t) %H(t) %L(t) %O(t) 4-1
4-1 (Average: x ) (Standard Deviation: SD) SET Index 1% 0.7%
SET TPI PLE
ITD KEST BNT
TPC EWC EMC
69
4-1 9 %C(t) %H(t) %L(t) %O(t)
SET x 0.048362 0.948878 -0.69075 0.175048 SD 1.740028 1.379424 1.420962 1.117595
TPI x 0.115644 3.481582 -2.17867 0.607003 SD 4.928221 4.153348 3.55573 2.153243
PLE x 0.117569 2.215106 -1.69384 0.253207 SD 3.418022 2.849696 2.350122 1.351423
ITD x 0.099832 2.681577 -1.81365 0.455979 SD 3.855195 3.285184 2.669347 1.668103
KEST x 0.000853 2.088855 -1.73778 0.177172 SD 3.239453 2.497072 2.11172 1.353322
BNT x -0.00391 3.520825 -2.2776 0.570123 SD 5.365825 4.628118 3.832043 2.21521
TPC x 0.128701 1.298008 -0.93925 0.040053 SD 3.293238 3.227688 2.831525 2.528116
EWC x 3.186381 6.557956 -0.91674 2.70946 SD 68.51091 71.38972 56.89548 67.7972
EMC x 2.810785 6.303126 -0.17303 2.90933 SD 71.0059 89.89276 66.76759 83.66099
4-2() 4 SET Index 4-2() KEST
4-2 4 SET KEST
0
100
200
300
400
500
-21 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 29
%C distribution(SET index)
0
100
200
300
400
500
600
700
800
-21 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 30
%H distribution(SET index)
0
100
200
300
400
500
600
700
800
-22 -10 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 28
%L distribution(SET index)
0
200
400
600
800
1000
1200
-22 -7 -6 -4 -3 -2 -1 0 1 2 3 4 30
%O distribution(SET index)
()
70
4-2 4 SET KEST ()
4-1 4-2 5
4.1.2
(Open: O) (Close: C) (High: H) (Low: L) (Volume) (Value)
0
20
40
60
80
100
120
-10 -8 -6 -4 -2 0 2 4 6 8 10
%C distribution(KEST)
020
406080
100
120140160
-4 -2 0 2 4 6 8 10 12
%H distribution(KEST)
0
20
40
60
80
100
120
-13
-12
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2
%L distribution(KEST)
0
50
100
150
200
250
300
-11 -9 -6 -5 -4 -3 -2 -1 0 1 2 3 4
%O distribution(KEST)
()
71
{C H L O}
(P Plane) (4-1)
{ } { } == ;,,,,,, VolValOLHCDX xin P (4-1)
(4-2)
( ) += ;,,, *OLHCGYout P (4-2)
( )G NFSV (Neuro-Fuzzy with Support Vector Guideline System) ( 4.4) P (4-2) { }OLHC ,,, ( )G { }OLHC ,,, (4-2) (4-3)
{ } { } { } { }( ) += ;,,,,,,, ** OLHCout OLHCGY P (4-3)
P
( )G (Inconsistence) inX
outY ( )G P
(Present Value Transform) (4-4)
72
( ) +=
=;,,, *
0itititit
k
ioutOLHCGY Q (4-4)
k
Q
4.4
4.2 2 3 AMA SVR ANFIS
2 3
AMA AMA
SVR (Global Approximation)
ANFIS (Subtractive Clustering)
73
SVR
SVR ANFIS AMA
4.3
SVR ANFIS 2 OCB (Output Component Based SVR) [35] OII (Output-Input-Iteration) [36]
SVR ANFIS
4.3.1 OCB (Output Component Based SVR) jtrnX itstX
SVR
( ) ( ) ( ){ }nn YtrnXYtrnXYtrnXtrn ,,,...,,, 2211= ( C ) tstX SVR SVR
SVR SV
74
SVR OCB (Output Component Base - SVR)
OCB (Algorithm) OCB OCB 2 (Training Mode) (Testing Mode)
OCB SVR 2.4.2.1 3
1. C C (2-27) (2-31) (2-31) 2-9 (2-39)
2. Weighting Vector trnX (Lagrangian) (Quadratic Optimization Problem)
3. C trnX SVR OCB
OCB 10 1. STP = 0 2. P L (tstX) 1
L
=
=jiSTPjitstX
P jji ::,1
,
3. SVR P L 3 SVR SVR
4. 3 =
1
1
L
ii
75
5. STP = STP + 0.1 6. 2 5 STP < 1.0 7. 4
8. STP 7 9. STP 8 STP
4 0 cubic spline interpolation
10. 1 =1
1
L
ii 8 SVR
tstX OCB
4-3 SVR() -SVR STP() (Stepping Function) SUB() SPL() (Cubic Spline Function) GSE
4-3 OCB
I1 I2
IL
Y
I1 I2
IL
trnX
tstX I1 I2
IL
I1 I2
IL I1 I2 I3
IL
SVR( ) STP( )
1
2
3
L
0 ( - ) 1,2
0 ( - ) 1,3
0 ( - ) 1,L
0 ( - ) 2,3
0 ( - ) L-1,L
L-1
WIN
SUB( ) SPL( ) (1)
(2)
=
1
1
L
i
i
SVR( ) trnX
tstX
76
itrnX jtstX { }lIII ,...,, 21 l n
(Spline) 0
SVR - SVR (4-5)
bvtstXtrnXkSVR ii += )( (4-5)
=
=jijitstX
vtstX ji :1..0:
(4-6)
* = b ( )k b 0 L l Lji
77
SUB() (4-7) 4-5
jik SVRSVRSUB = (4-7)
( )
>+
==
=
1
11:
1:L
mimLij
iijk SUB()
=
1
1
L
ii
4-4 l 4 6 4-5
4-5 SUB( )
4-6 itrnX jtstX
(4-6) (Cubic Spline Interpolation Function) (x, y) (Knots)
STP( )
SUB
( )
78
x = SUB() y x = 0 4-5 4-6
4-6 SUB( )
=
1
1
L
ii
WIN( ) 4-7 4-6
OCB SVR SVR 4-7
SPL() SVR() OCB
WIN( ) 4-7 (4-8)
( )( )
=
trnX,minarg tstXSVRiSUBabsi
SPLWIN (4-8)
STP( )
SUB
( )
79
arg i SUB( ) SVR( )
4-7 WIN( )
OCB
SVR trnX OCB PTT
2 trnX 4/1/48 30/6/48 65 2 tstX 3/7/49 22/8/49 34 { }openlowhighclose ,,, max = 259.2
C = 100 = 0.001 = 1 trnX 4-8 2 mse (Mean Square Error) Ustat (U-Theil) [37] (-1) (-2)
4-8 SVR [mse, ustat] = [0.013792, 8.3036] OCB [mse, ustat] = [0.000296, 1.0507] SVR
trnX
0
( - )
0
( - )
0
( - )
SPL( ) (1)
(2)
=
1
1
L
ii
Selector
tstX
Min. Idx
WIN( )
Y
SVR( )
80
OCB
4-8 SVR OCB
OCB SVR
OCB SVR SVR
4.3.2 OII (Output Input Iteration) OII
OII OII OII 2 (Training Mode) (Testing Mode)
OII ANFIS 2.5.3.1 8
1. ar (2-57) 2-16 8/ar=
day
Nor
mal
ized
out
put
81
2. Potential Value iP (2-58) (Subtractive Clustering)
3. 2 P trnX 4. P 3 c
Fuzzification Layer (2-51) 5. (Output-MF Consequent
Parameters) k (2-68) (Least-Squares Estimation) (Moore-Penros Pseudo Inverse)
6. (2-72) e 7. c (2-51) (Input-
MF) (Gradient Descent Method) (Derivative Chain Rule) (2-72)
8. 5 7 e 9. c k
ANFIS OII OII 9
1. STP = [0..1] inc = 0.1 n STP
2. [ ]
=
==
= j
n
jj
jnii STPjiSTP
jitstXP ,1
1,1
,11 ,:
:
tstX 3. ANFIS P n
9 OII OII
4. 3 STP n 5. STP 4
82
6. STP 5 inc = inc / 0.1
7. e 8. 2 6 inc > e 9. OII 5
OII (4-9) (4-17) (4-9) x n
[ ] ni xxxx ,,, 21, L= (4-9)
ni 1
OII ANFIS (4-10) (4-11)
[ ] yxxxx nn
ii,,,, 211, L== (4-10)
OII
(Sugeno Fuzzy: TS) (4-11)
( )( )
( )
=
n
i
nn
n
n
VVxx
VxVxVxxV
x
)stp()stp(21
2)stp(
2)stp(
21
1)stp(
12)stp(
1
)f(,
,,,,
,,,,,,,,
L
MLM
L
L
(4-11)
)f(x )stp(V )inc(V (4-14)
0)low( =V 1)hgh( =V 1.0)stp( =V { },,ck ANFIS (TS) (4-12) (4-14)
)stp()out()dif(
iii VVV = (4-12)
83
{ }( ) ,,,TS )f( ,)out( ckxV ii = (4-13)
)inc()stp()stp(1 VVV rr +=+ (4-14)
ni 1 n n
VVV)low()hgh(
)inc( =
)hgh()stp()low( VVV
)low(V )hgh(V (4-15) (4-16)
( ))inc()low( -OIInew VV = (4-15)
( ))inc()hgh( OIInew VV += (4-16)
OII
(4-17) )dif(V )dif(V
- 2 (4-16) (4-17)
{ }( )
( ) ( )
+
==
otherwiseVV
VVV
c,k,x,Y
iViV
iVii
;
00;
recurOII)(
arg
)(
arg
)dif()dif()(
minarg
out
stp
0)dif(max
stp
0)dif(min2
1
stp)dif(
(4-17)
out recur (recursive) OII() OII
)dif(V OII )dif(V
84
OCB OII
4.4 NFSV (Neuro-Fuzzy with Support Vector Guideline System)
NFSV ANFIS SVR
ANFIS SVR
4-9 NFSV 2 (Training Mode) (Testing Mode)
4-9 NFSV
Training data
Reform & Normalization
Data jittering
SVR & OCB learning
ANFIS & OII learning
Training Mode
Parameters
Testing Mode
Testing data
Reform & Normalization
SVR & OCB Predicting
Reform & Normalization
ANFIS & OII Predicting
Stock rules Filtering Not pass
Pass
Predicted Output
Parameters
ANFIS & OII Re-learning
85
SVR OCB ANFIS OII (Jittering) ANFIS OII
SVR OCB ANFIS OII
NFSV
ANFIS OII
4-9 NFSV 6
1. (unit value) 2. SVR OCB 2.4.2.1
SVR 3. widowing size = 4 4. 3 3 Jittering
1 5. 4 ANFIS OII
3 6.
86
10 1. 2. SVR OCB 3. 2 4. Windowing Size = 4
3 5. 4 6. ANFIS OII
3 3 7. 6 Stock
Rules 8. 7
ANFIS OII
9.
10. 7 NFSV
NFSV
NFSV 6
- - - - - - 4.4.1 (Long Term)
87
SET Index of Thailand .. 2545 2549 4-10
4-10 SET Index of Thailand 4-10() 5 300
700 ACF (Autocorrelation Function) 4-10() 360 1 (Gaussian White Noise) 19 2549
SET Index
0100200300400500600700800900
2/1/
2545
5/4/
2545
17/7
/254
5
21/1
0/25
45
29/1
/254
6
9/5/
2546
15/8
/254
6
18/1
1/25
46
24/2
/254
7
7/6/
2547
10/9
/254
7
16/1
2/25
47
23/3
/254
8
5/7/
2548
7/10
/254
8
13/1
/254
9
24/4
/254
9
2/8/
2549
7/11
/254
9()
() ()
88
721 622 5
(Space Shifting Method)
(H-Space) (T-Space) 4-11
4-11 4-12 H1
P1 T1
H-space
T-space
Time System learning
89
4-12
4-13() 4-13()
4-13
Time T-space H-space Time T-space
System learning
H-space
() ()
H1
T-space
Time
System learning
P1
T1
90
(Backward Adaptation Control) (Forward Prediction Control) 2
4-14 3 (Pre-Data & Control Part) (Forward Prediction Part) (Backward Adaptation Control Part) NFSV
4-14 NFSV 2 (Pre-
Data) (Selector)
91
U&A (Unify and Data Arrangement) OCB U&A OCB
3 G-OII (Global Output-Input-Iteration) FC(Forward Control) UPL (Upper-Prediction-Lower)
G-OII ANFIS ANFIS
G-OII G-OII FC G-OII (Stock Rule)
UPL UPL
(Backward Adaptation Control Part) 3 GLC (Global-Local Condition Control) SFR (Slope-Filter for Retraining) ANFIS
GLC 2 GC (Global Control) LC (Local Control) G-OII OII
92
GC LC
GC OII OCB LC
SFR GLCC SFR
4.4.2
(4-18) k C H L O k
{ } { } { } { }{ }kttOkttLkttHkttCX ===== :1);(,:1);(,:1);(,:1);( (4-18)
93
(4-19) m
{ } { } { } { }{ }mttOmttLmttHmttCY :1);1(,:1);1(,:1);1(,:1);1( =+=+=+=+= (4-19)
(4-19) (4-20)
{ })1( += tCY (4-20)
(4-18) (4-20) (4-21)
{ } 10;,,
== DXXD
1)-C(taY
1)-C(taYu (4-21)
a D (4-22)
( )
( )
=
myxxx
yxxxD
k
k
,,,,
,,,,
421
1421
L
MLM
L
(4-22)
(4-3) (4-4) (Loose Recursive Slope Filtering) (4-23)
[ ] [ ]
>
>
>
=)(Asc
11;recur
421
ymr
syx
syx
syx
DT
jr
kj
rj
r
j
L (4-23)
94
s ( )yAsc (Ascended Ordering)
jrecur j
(4-23) C H L O k y s ( s )
s
D j ( )uYAsc (4-23) 1+js 1+j
jT D Y D 4-15
4-15 jT (4-23)
95
X Y (4-24) (4-26) (4-27)
( )= =
=m m
srr s
ddm
a1 1
cosi1 (4-24)
( )( )sss
DDDDDD
dddrr
rrssr
== 1)(icos (4-25)
m d (4-25)
( )( )1(max) ++= mvab (4-26)
( )( )1(min) += mvab (4-27)
( )rsdSDv = msr ,,2,1, L=
(4-28)
(max)(min);' bbDD rrr = (4-28)
=
=m
rsrs
d1
(4-29)
(4-29) r r (min)b (max)b
96
(Forced Data Jittering) T
(4-22) T 1 (4-30)
m,
r== ir DD (4-30)
T,,2,1 L=r T
ANFIS
4.4.3 NFSV 4-14
3 4-16
NFSV 4 4 4-16
(Jittering) N (Network) N
97
1
4-16 NFSV
T (Node)
NFSV N T R R
ANFIS SVR { }ck ,, ANFIS { } ,,C SVR 4-16
4.4.4 (Pre-Data & Control Part)
Network 1 NNode 1 TRule 1 R
Fuzzy ]R[ = n ]R[ = nc
)]1(R[ += nk
(C)(H)
(L)(O)
SVR parameters ,,C
98
4-17 (Testing Mode) trn { }ck ,, tst 3 trn tst (4-31) (4-32)
{ }1OCB)( 1333,31u(Z)t ,,,...,,,...,,,...,,...,,trn ++= ttttttttttt ZCOOLLHHCCC (4-31)
{ }Null,,,...,,,...,,,...,,...,,tst OCB)( 1333,31u(Z)t += tttttttttt COOLLHHCCC (4-32)
Z { }OLHC ,,,
4-17
( )CN,s t
{ }( )C1,i
U&A
( )( )( )RHistst ( )Ctst t
OCB
( )R(tst) t ( )Htst t( )Ltst t ( )Otst t
( )OCB1tst +t
( )Ctst t
trn
( )Ctrn
( )C1,s t
{ }( )C1,,, ick
{ }( )CN,',, ick { }( )CN,i
(O)
(L)
( )Otrn
( )Otst t
{ } )C(,, ck
{ } )O(,, ck
{ }( )HN:1
{ }( )LN:1
{ }( )ON:1
(H)
(C)
Selector
( )Otrn
( )Ltst t
ttst
To G-OII, LC, and SFR unit
( )Htst t
{ }c,k,
99
{ }OLHC ,,, 3 OCB ( 4.3.1)
(4-31) (4-32) 1+tZ tst Null
N { }OLHC ,,, T l l T N (4-33) (4-34)
[ ] [ ] [ ]
[ ] [ ]
[ ] [ ] [ ]
=
)Z(N,T
)Z(,T
)Z(1,T
)Z(N,
)Z(1,
)Z(N,1
)Z(,1
)Z(1,1
)(
lj
ll
il
il
lj
ll
Z
LL
M
M
M
LLL
M
M
M
LL
trn (4-33)
[ ]
{ }
{ }
{ }jittttttttttt
lttttttttttt
ttttttttttt
jil
mZCOOLLHHCCC
ZCOOLLHHCCC
ZCOOLLHHCCC
,1OCB)(1333,31u
1OCB)(1333,31u
11OCB)(1333,31u
)Z(,
,,,...,,,...,,,...,,...,,
,,,...,,,...,,,...,,...,,
,,,...,,,...,,,...,,...,,
=
++
++
++
(4-34)
u ( u ) T,2,1 L=i N,2,1 L=j N T R
100
trn ( 4.4.2)
[ ] )Z(, jil
(Subtractive Clustering) radii 30% l 4-18 rule/lmax radii radii 30%
4-18 radii NFSV { },,ck
(4-35) (4-36) trn
{ }{ } { }
{ } { }
=)Z()Z(
1,T
)Z(N,1
)Z(1,1
)(
N,TL
MMM
LZc,k, (4-35)
{ }
)(
,
)(R,
)(1,
)(R,1
)(1,1
)(R,
)(1,
)(R,1
)(1,1
)(R,1
)(1,1
)(R,
)(1,
)(R,1
)(1,1
)(,
' Z
ji
Zn
Zn
ZZ
Zn
Zn
ZZ
Zn
Zn
Zn
Zn
ZZ
Zji cc
cc
kk
kk
kk
=
++
K
MKM
L
K
MKM
L
K
K
MKM
L
(4-36)
radii
errorRule/ lmax
101
selector (4-37) tst l [ ] )Z(, ji
l
l trn { },,ck i j tst
[ ]
= )Z(,
(Z)t
)(, ,tstcosimin; ji
li
Zjt iS (4-37)
T1 )(, ZjtS )cosi( (4-25) x s y x y
trn
G-OII (Global Output-Input Iteration) LC (Local Control)
4.4.5 (Forward Prediction Part) 3 G-OII
FC UPL 4-19 U&A Selector
G-OII FC UPL UPL
102
2 ( )Ztst t { }( )Z, ji OII G-OII OII N G-OII 4 N (4-38)
4-19 NFSV
{ }),( 1,( 1),( 1),( 1),( 1)( ,,, jPtjPtjPtjPtjPt OLHCZY +++++ ==GOII (4-38)
Z 4 { }OLHC ,,, P 1+t N,,2,1 L=j N
OII (4-38) (4-39) OII 2 ( )Ztst t { }( )Z, ji OII 4.3.2
{ }( ))Z(,(Z)),( 1 ,tstOII jitiPtZ =+ (4-39)
N
(Local Minimized)
{ }( )ZN,'i
OII
OII
( )P,11Z +t
( )NP,1Z +t
{ }( )Z1,i
( )Ztst t G-OII
( ),1*P1Z +t
( )q,*P1Z +t
( )RC t
FC UPL
( )P1Z +t
( )UP1Z +t
( )LW1Z +t
{ }( )Z, ji
From U&A and Selector unit
Prediction output
103
OII FC (Forward Control) 4-19
(4-40)
)R()R(),P( 1 tt
jt CCZ + (4-40)
R )R(max )1( tC+= )R(min )1( tC= %30 )R(tC
(4-40) (4-40) (4-41) FC
=
=+
1
),Z(),Z(1
)( tsttstSD2t
njt
jt
Zj (4-41)
(4-41)
),Z( 1tst jt+ ),Z(tst jt FC (4-42)
= +++ rjjZZZZ
Zjt
jtj
tr
t ;min;
)()R(),P(1),P(
1),P(
1
* (4-42)
qr ,,2,1 L= N,,2,1 L=j N
104
-
FC (4-43) (4-44)
),P(
1)UP(
1*
max rtrt ZZ ++ = (4-43)
),P(
1)LW(
1*
min rtrt ZZ ++ = (4-44)
UP LW qr 1
(4-45) (4-46)
++ =s
st
rtr ZZ
),P(1
),P(1
** (4-45)
( )( )
( )
==
=
++
++
++
+
+
)LW(1
)LW(1
)UP(1
)UP(1
)UP(1
)LW(1
dx
MODEdx,
1
)P(1
spl;spl;
spl,;,,spl;
*
tt
tt
ttji
ri
iP
t
t
ZZZZ
ZZxxyxZ
Zs
(4-46)
qsr ,1 spl idid,P
1
*
+= tZy
( )unq=x ( )idxid = =xx Z { }OLHC ,,, idx q )idx(1
(4-46) r ),P( 1
* rtZ +
rrtZ + ),P( 1*
(MODE) ),P( 1* r
tZ + r r
105
r ),P( 1
* rtZ +
),P( 1* r
tZ +
),P( 1* r
tZ + r (4-46)
NFSV 4-14 (4-31) (4-46) 4-20 4-21
4.4.6 (Backward Adaptation Control Part)
[38] [39]
(Temporal) 3 (S) (D) (R) 4-22
4-22
T
Time
S
D
R
S
D
R
106
4-20
{ }( )CN,'i
( )CN,s t
{ }( )C1,i
U&A
( )( )( )RHistst ( )Ctst t
OCB
( )R(tst) t ( )Htst t ( )Ltst t ( )Otst t
( )OCB1tst +t
OII
OII
( )P,11C +t
( )NP,1C +t
{ }( )C1,i
( )Otst t
( )Ctst t
trn
{ }c,k,
( )Ctrn
( )C1,s t
{ }( )C1,,, ick
{ }( )CN,',, ick { }( )CN,i
(O)
(L)
( )Otrn
( )Otst t
{ } )C(,, ck
{ } )O(,, ck
{ }( )HN:1
{ }( )LN:1
{ }( )ON:1
(H)
(C)
Selector
( )Ctst t
(C)
G-OII
( ),1*P1C +t
( )q,*P1C +t
( )RC t
( ),1*P1+tO
( )q,*P1O +t
( )RO t
FC
( )( )( )RHistst
( )P1C +t
( )UP1C +t
( )LW1C +t
UPL
(O)
(O)
(L)
(H)
(C)
( )P1H +t
( )UP1H +t
( )LW1H +t
( )P1L +t
( )UP1L +t
( )LW1L +t
( )P1O +t
( )UP1O +t
( )LW1O +t
( )N:P,11O +t
( )Otrn
( )Ltst t
ttst
( )Htst t
106
107
4-21
( )P,11C +t
( )P,11H +t
( )P,11L +t
( )P,11O +t
( )R1C +t
( )R1H +t
( )R1L +t
( )R1O +t
( )NP,1H +t
( )NP,1L +t
( )NP,1O +t
( )NP,1C +t
(1)
( )C1mk
(N )
( )H1mk ( )L1mk ( )O1mk
( )CNmk ( )HNmk ( )LNmk ( )ONmk
GC
ttst
(C)tst t
( )C1mk
( )R1C +t
( )C1mk'
( )CNmk
( )R1C +t
( )CNmk'
(C)
(H)
(L)
(O)
LC
(1)
ANFIS ( )C1trn
( )CNtrn
SFR Retrain
(1)
(N )
( )Ctst t
( )C1,trn'i
{ }( )C1,i
(N)
ANFIS
( )Ctst t
( )CN,'trn'i
{ }( )CN,'i
(C)
(H)
(L)
(O)
( )C1mk' ( )C
1,trn"i
{ }( )C1,'i
( )CNmk'
(C)
( )CN,'trn"i
{ }( )CN,''i
G-OII
Stock pricing sources
(H)tst t (L)tst t (O)tst t
U&A
Selector
(C)tst t
(C)tst t
( )C1mk'
( )CNmk'
ttst
trn(H)
(L)
(O)
107
108
(T) (S) (D) (R) (4-47)
)()()()(1 SRDRSRDSDD t == + UIUIUII (4-47)
4-23 S D R (Exactly Believed) (Hesitated) S D
4-23 S R
(Believed) D R (Self-Believed) D R (Confused Decision) R (Not Believed)
(R)eal occur
Hesitated
Exactly Believed
Self-Believed Believed
Not Believed
Confused Decision
Confused Decision
(D)ecision(S)uggestion
109
S (Upper Bound) (Lower Bound) 4-24 D S (Prediction) (Actual Value) R
(Not Believed) (Confused Decision Areas)
(Exactly Believed Area) (Believed Area) (Offset)
4-24 NFSV
day
value
110
),( 1 jPtZ + (4-38) G-OII )R( 1+tZ 2 GC (Global Control) LC (Local Control) G-OII LC G-OII 4-21
GC 4 G-OII (4-48) (4-51)
(4-48)
(4-48) (4-51) (4-49) (4-50) 4
( ) ( )
>>=
++++
++++
otherwiseHLOL
CLHL
tj
ttj
t
tj
ttj
t
j
;0)1(
;1mk )R(
1),P(
1)R(
1),P(
1
)R(1
),P(1
)R(1
),P(1
)L( (4-50)
( ) ( )
>
112
)(,ZjtSr = (4-37) T1 r { } )Z( , jr (4-36) Selector (Z)tst't (4-54) (4-32) Null R)( 1+tZ OII
{ }R)( 1OCB)( 1333,31u(Z)t ,,,...,,,...,,,...,,...,,tst' ++= ttttttttttt ZCOOLLHHCCC (4-54)
LC 2
GC OCB OII OCB OII GC
OCB ( )> ++ )R( 1)( 1OCB tZt Z ( )
113
r 1 )( ,trn Zjr )(tst Zt
l (Z)ttst [ ] )Z(, ji
l
l )( ,trn' Zjr { } )( ,Zjr r j Z (4-56) (4-57)
{ } { }( )
[ ]
[ ]
=
=
=
=
=
)Z(,
(Z)t
)(,
)()Z(,
)(,
)(,
1Z)(,
)(,
1Z)(,
,tstcosimin
tst
;,trn'ANFIS'
trntrn')('
)('
mk
mk
jili
Zjt
Ztjr
l
Zjr
Zjr
Zjr
Zjr
Zjr
l
Srj
j
(4-55)
{ } { } )( ,)( , 'new ZjrZjr = (4-56)
)(,
)(, trn'trnnew
Zjr
Zjr = (4-57)
4.5 NFSV
NFSV OII OCB ANFIS SVR
OCB SVR OII OCB OII
114
ANFIS SVR NFSV
NFSV
NFSV SVR ANFIS OII OCB U&A
NFSV
NFSV
(Wavelet) OCB
NFSV 5
5
NFSV
NFSV 2 NFSV
5.1
3 3 NFSV
AMA 5.1.1
SVR OCB 5.1.2
OII ANFIS OII ANFIS 5.1.3
5.1.1 AMA AMA 2.3.4 2.6.1
AMA NFSV
116
5.1.1.1
5.1.1.1 AMA 7
10 4 4 2548 24 5-1 5-2
3 (Trend) (Season)
2 Linear: Up-Down { 2 18 ; 1 8 1.5 3 ; 9 24x xy x x + == = KK { 3 1 ; 1 141 56 ; 15 24x xy x x+ == + = KK Linear: down-up
3 Season: Up-Down 1 {219, 216, 218, 185, 154, 147, 124, 93, 127, 148, 161, 198, 236, 239, 221, 194, 161, 131, 110, 101, 131, 157, 189, 217} Season: Up-Down 2
)2sin()sin( xxy += Season: Diverting )10sin()3sin( xxxy =
5-1 AMA
2 Season: up trends {362, 385, 432, 341, 382, 409, 498, 387, 473, 513, 582, 474, 544, 582, 681, 557, 628, 707, 773, 592, 627, 725, 854, 661}. Season: Down Trends Season: Up Trends
117
5-2 10 AMA
5.1.1.2 AMA Linear-Down-Up AMA
(Adaptive Approach) 5-3 1.01.01.0 === 1.0= 5-3()
5-3()
5-3
() ()
118
89.0= 71.0= 42.0= 79.0= 5-4
5-4 AMA 1 5-5 AMA 2
64.0= 1= 61.0= 6.0= AMA 1
5-5 AMA 2 5-6() () tC NtC ,1+ tG tS
(2-2) (2-7) AMA 1 2 5-6() 5-6() (2-7) (2-8) tC 2 Ft tC (2-8)
119
tS tG 1 2
5-6 AMA 1 2 5-7 Up-Down 1
1 2 AMA 1 2
5-7 AMA 1 2
( ) ( ) NtCtStDtC ,1/ +=
() ()
() ()
120
5.1.1.3 AMA AMA 5.1.1.1
5-1 5-2
5-2 AMA 1 2 diverting AMA 1 1
(Moving and Smoothing Method) AMA 1 2 1 Ft 2
5-1 AMA
121
5-2 AMA 1 2
2 SD( )
(2-8) AMA 1
NFSV 5.3 5.1.2 SVR OCB SVR OCB
OCB 4.3.1 NFSV Mackey Glass Time Series [40, 41]
5.1.2.1 SVR OCB
2 k 1.3
122
m (5-1) (5-2)
{ }kiikiikiikiiii
i OOLLHHCCCCX
= ,,,,,,,,,,,,3.11
1 LLLL (5-1)
{ }iiiiii OLHCVVX
X ,,,1
maxmax
== (5-2)
i k { }OLHC ,,, )max(max datatrainmV =
SET 5.1.3.1 5-8 5-9 5-3 5-4
5-11 REC [42, 43] SET (5-1) SVR OCB k = 2 3 4 SVR OCB SVR SVR OCB 5-9 (5-2) m = 1 1.5 2 SVR OCB m = 1.5 m = 1 m = 2 OCB SVR
5-8 5-9 5-3 5-4 AOC (Area Over the Curve) [42, 43] mse (Mean Square Error) Ustat (U-Theil) [37] OCB (5-1) (5-2) (5-2) m = 1.5 OCB AOC SVR mse Ustat mse Ustat AOC SVR OCB 5-10
123
k = 2 k = 3 k = 4 C
LOSE
HIG
H
LO
W
OPE
N
5-8 SVR OCB SET (5-1)
REC
5.3 5-8 k = 2 k = 3 k = 4
AOC mse Ustat AOC mse Ustat AOC mse Ustat OCB 18.85 805.79 2.1728 24.926 1339.1 2.8353 73.778 7178.6 6.3501 CLOSE SVR 60.475 3885.3 4.6621 71.904 5498.2 5.526 99.66 10424 7.5863 OCB 28.442 1309 5.5979 45.263 2885.6 8.2594 57.048 4125.7 9.8859 HIGH SVR 87.036 7740.3 13.43 99.612 10146 15.312 85.359 7467.2 13.218 OCB 45.304 3836.4 3.9617 102.34 14053 7.7381 104.89 14085 7.8079 LOW SVR 117.1 14099 7.7667 136.88 19378 9.0858 179.5 33183 11.873 OCB 22.37 1361.5 3.139 55.117 5334.3 6.289 80.859 9053.8 8.0815 OPEN SVR 131.72 17610 11.158 159.48 25940 13.484 163.38 27204 13.788
124
m = 1 m = 1.5 m = 2
CLO
SE
HIG
H
LO
W
OPE
N
5-9 SVR OCB SET (5-2)
REC
5.4 5-9 m = 1 m = 1.5 m = 2
AOC Mse Ustat AOC mse Ustat AOC mse Ustat OCB 6.9814 201.3 1.0726 7.0351 192.72 1.0316 9.1284 270.19 1.2314 CLOSE SVR 96.866 10262 7.4141 8.3139 212.71 1.0909 30.402 1108 2.4964 OCB 6.9684 121.17 1.6936 4.1187 34.657 0.92479 7.1824 111.28 1.6637 HIGH SVR 66.199 4867.5 10.486 4.2087 35.13 0.93236 17.738 353.51 2.9357 OCB 6.4242 319.68 1.2348 6.2609 240.46 0.99964 10.023 354.58 1.2269 LOW SVR 77.028 6583.5 5.2009 6.2978 234.27 0.98806 26.407 935.91 2.0194 OCB 4.8411 80.656 0.766 3.0462 19.148 0.38773 6.0688 53.367 0.63246 OPEN SVR 70.286 5602.3 6.1781 3.382 22.075 0.41637 32.381 1084.9 2.8207
125
5-10 SET (5-2) m=1.5
(Classification)
SVR OCB (5-9) NFSV
5.1.2.2 Mackey Glass Time Series OCB
Mackey Glass Time Series [40] 1 (5-2) OCB (4-7) 0 OCB (5-1) (5-2) k = 1 5-11
126
SVR OCB
5-11 SVR OCB Mackey Glass time series
Mackey Glass Time Series OCB
5.1.3 ANFIS OII NFSV ANFIS OII
SVR OCB OCB 5.1.2
ANFIS OII NFSV SVR OCB
5.1.3.1
127
5.1.3.1 10
2 1 SET index 5 BBL(BANGKOK BANK
PUBLIC CO.) KTB (KRUNG THAI BANK PUBLIC) SCB (THE SIAM COMMERCIAL
BANK) TISCO (TISCO BANK PUBLIC CO.,LTD.) TMB (TMB BANK PUBLIC CO.,LTD.)
4 IRP (INDORAMA POLYMERS PUBLIC) PTTCH (PTT CHEMICAL PUBLIC) TPC (THAI PLASTIC AND
CHEMICAL) VNT (VINYTHAI PUBLIC CO.,LTD.) 3
2549 28 2550 3 2549 28 2549 1 2549 28 2550 5-12
NFSV 19 2549 SET 108.41 730.55 622.14
SET NFSV 5-12
128
5-12
() SET () BBL
() KTB () SCB
() TISCO () TMB
() IRP () PTTCH
129
5-12 ()
5.1.3.2 ANFIS OII (5-1) (5-2) (5-2)
2.6.3 (5-1) k = 3 OII (4-10) k (5-1)
ANFIS OII 2.5.3.1 radii
SET NFSV 4.4.2
5.1.3.3 ANFIS OII 4 (C) (H)
(L) (O) )(SY )(RY )(IY (5-3)
() TPC () VNT
130
==
=
n
ji ii
jjiRI
RS
YY
YY
nYinYout
1,1
,
)(
)(1/ )()(
)()(
2 (5-3)
i Step Value 0 1 n i
0)( SiY Y ( ) (S) (Step Output) (R) (Real Actual Value) (I) (Input)
)(IY SVR OCB OII (5-3) 0 OII 1 )(IY 1 OII
(5-3) 0 1 SVR OCB 0
ANFIS OII SET 4.4.2 {C H L O} (MF Rule/Train data ) 5-13
5-13 ANFIS OII
131
0.4 0.5 NFSV 5-13 ANFIS (L) MF Rule/Train Data = 0.4 0.5
4 3 MF Rule/Train Data = 0.1 0.5 1.0 5-14
MF rule/Train data 0.1 0.5 1.0
CLOS
E
HIGH
LOW
OPEN
5-14 MF Rule SET
132
MF Rule/Train Data = 0.1 OII ANFIS )(SY )(IY NFSV 1.0
MF Rule/Train Data = 0.5 )(IY OII ANFIS (L) ANFIS ANFIS_L 5-13 ANFIS 4.2.3
MF Rule/Train Data = 1.0 (2-64) (Forward Learning) 1 )(IY 0
NFSV 4.4.1
Rank Benefit (RB) (5-4)
==
=n
ijjiCn
RB1,1
,21 (5-4)
1, =jiC 0)(, >SjiY
5-15 MF Rule RB ANFIS OII OII
133
5-15 RB MF Rules Ustat
(Train Data) SET Ustat 5-5 5-5 Ustat SET
Ustat 1+tC 1+tH 1+tL 1+tO
tC 1 1.1384 1.0451 0.37128tH 1.2363 1 1.6387 0.81268tL 1.2809 1.9664 1 0.95467tO 1.3349 1.6367 1.4809 11tC 1.381 1.6604 1.533 1.09841tH 1.547 1.5219 1.9524 1.26091tL 1.6008 2.2565 1.4159 1.32881tO 1.6848 1.9985 1.8258 1.36622tC 1.6729 2.0135 1.8154 1.38452tH 1.8256 1.9531 2.1792 1.54072tL 1.8206 2.5407 1.7401 1.61092tO 1.9035 2.3948 2.0761 1.6793tC 1.8662 2.4591 2.0687 1.64823tH 2.0781 2.4596 2.4769 1.83063tL 2.0195 2.9566 2.0152 1.83763tO 2.1492 2.8291 2.3979 1.9389
134
Ustat {C H L O} 1 3 {C H L O}
Ct Ht Lt Ot Ustat 1 Ct+1 Ht+1 Lt+1 Ot+1 Nave 5-5 1 1+tO tC
5.2 1
5.1 1 OCB OII SVR ANFIS
1 NFSV 5.2.1 5.2.2 5.2.3 5.3 NFSV
5.2.1 1
NFSV
2 SVR OCB ANFIS OII 6
- S-ANFIS - S-OII - O-ANFIS - O-OII - M-ANFIS
135
- M-OII S- SVR O- OCB M- SVR OCB 1 ANFIS OII 5-16
2 OII ANFIS OII ANFIS (5-5) (5-10)
5-16 1
{ }iiiii OLHCX ,,,= (5-5)
{ }kiikiikiikiiii
i OOLLHHCCCCX
= ,,,,,,,,,,,,3.11
1 LLLL (5-6)
{ }iiiiii OLHCVVX
X ,,,1
maxmax
== (5-7)
{ })()(,, ,, STPjINijiji YYYX = (5-8)
==
=jiYXjiX
Y STPiji
jiji ;
;)(
,
,, (5-9)
X 'X
''X '''XSV
ANFIS
ANFIS
STP
(-)
SUB
=0 PD
Output
Input
OII
136
{ }
=typeMfor;,
typeSfor;typeSfor;
)()(
)(
)(
)(
OUTi
OUTi
OUTi
OUTi
INi
OCBSVROCBSVR
Y (5-10)
ki L1= j iX k Vmax )(INY )(OUTSVR S-type )(OUTOCB O-type 2 M-type )( STPY step value 0 1
SUB( ) 0 (4-17) Hybrid Secant False Position Method [44]
NFSV 5.1.3.1 TMB IRP 2 5-12() ()
iX (5-7) (5-11)
iTRN
TST
i XXXX
)(
)(
new = (5-11)
)(TSTX )(TRNX max value (5-7) (5-2)
2 (SV) (5-11)
5.2.2 (5-11) SET
m (5-2) (5-11) 5-17 5-19 Ustat 5-6
137
(5-11) SET TMB IRP
SET TMB SVR OCB SVR OCB
() m = 1 () m = 1
() m = 1.4 () m = 1.4 5-17 SET m IRP
SVR OCB
5-7
138
() m = 1 () m = 1
() m = 1.4 () m = 1.4 5-18 TMB m
() m = 1 () m = 1 5-19 IRP m
139
() m = 1.4 () m = 1.4 5-19 IRP m ()
5-6 Ustat 5-16 5-19 m = 1 m = 1.4
Adjust Non adj. Adjust Non adj. SET OCB 1.0726 3.0182 0.92832 1.3629
SVR 7.4141 15.667 0.95906 1.6933TMB OCB 1.1381 2.6266 1.7056 1.4741
SVR 1.5554 26.276 4.6818 13.742IRP OCB 3.1131 1.0648 3.6244 1.072
SVR 34.1 1.2669 10.003 1.4912
5-7 SET TMB IRP Train data (1) Test data (2) (2)/(1)
SET Average 742.5482 696.9454 Max. 764.01 746.16 0.976636 Min. 723.86 616.75
TMB Average 4.5985 2.761382 Max. 5.2 3.38 0.65 Min. 4.18 1.88
IRP Average 5.033833 6.902439 Max. 5.9 7.75 1.313559 Min. 4.7 5.3
5-6 5-7 SET m = 1.4
1 TMB m
140
1 1.4 0.65 IRP m Vmax (5-12)
)max(max datatestmV = (5-12)
(5-12) m = 1.4 OCB
Ustat SET TMB IRP 0.91844 1.1728 1.0999 SVR SUB (4-5) (4-7) 5-20 IRP 5-20() m = 1.4 5.20() m = 0.7 SUB
() m () m 5-20 (4-5) (4-7) m
5.2.3
5-16 Vmax (5-12) 10 5.1.3.1 Ustat m
ANFIS 3 (2-51) 0 2 ANFIS 0
141
2
10 7 BBL KTB SCB TISCO TMB PTTCH TPC 3 IRP VNT SET 2
4 5-21 Ustat 5-8 m
Average strenght of CLOSE price
01
2345
67
1 1.3 1.5 1.7 1.9
m
Ust
at
OCBSVRM-ANFISM-OIIS-ANFISS-OIIO-ANFISO-OII
()
Average strenght of HIGH price
012345678
1 1.3 1.5 1.7 1.9
m
Usta
t
OCBSVRM-ANFISM-OIIS-ANFISS-OIIO-ANFISO-OII
()
5-21 8 Ustat
142
Average strenght of LOW price
00.5
11.5
22.5
33.5
44.5
1 1.3 1.5 1.7 1.9
m
Ust
at
OCBSVRM-ANFISM-OIIS-ANFISS-OIIO-ANFISO-OII
()
Average strenght of OPEN price
0
0.5
1
1.5
2
2.5
3
1 1.3 1.5 1.7 1.9
m
Usta
t
OCBSVRM-ANFISM-OIIS-ANFISS-OIIO-ANFISO-OII
()
5-21 8 Ustat () 4 O-ANFIS O-OII
SVR OCB S-OII M-OII S-OII S- ANFIS
5-21() () SVR m 1.3 .15 m O-ANFIS O-OII
5-8 5.1.3.3
143
5-8 Ustat 5-21
Ustat 5-9 5-10
5-9
5-10
O-ANFIS O-OII
NFSV 2
Close High Low Open OCB 1.6957286 1.3047186 1.3648831 0.889202SVR 3.0542323 3.101438 2.2707357 1.463072
M-ANFIS 1.9417851 1.8966506 1.3232843 0.9515437M-OII 1.5969669 1.7116994 1.1578751 0.9218371
S-ANFIS 1.6289334 1.8016637 1.1843634 0.8720937S-OII 1.5210286 1.7731654 1.1262434 0.857188
O-ANFIS 1.1401269 1.0747871 0.9746497 0.7409369O-OII 1.1577966 1.0789766 0.9819391 0.7604249
Close High Low Open OCB 1.428169 2.495662 1.380369 0.723853SVR 3.230373 3.840028 2.324361 1.84349
M-ANFIS 2.148549 5.059182 1.511466 1.552062M-OII 1.941701 4.768675 1.465669 1.363345
S-ANFIS 4.887607 7.763607 3.542371 3.424673S-OII 5.576233 7.354653 3.560603 3.348343
O-ANFIS 4.600853 7.743513 3.487098 3.276581O-OII 5.03744 7.751627 3.498818 3.279216
Close High Low Open OCB 1.428169 2.495662 1.380369 0.723853SVR 3.230373 3.840028 2.324361 1.84349
M-ANFIS 1.919342 2.448329 1.340526 1.000922M-OII 1.630134 1.946448 1.217802 0.774169
S-ANFIS 1.669655 2.659717 1.181798 0.900395S-OII 1.612979 1.952613 1.208223 0.813836
O-ANFIS 1.219062 1.40326 1.090052 0.720306O-OII 1.242133 1.407927 1.105905 0.71858
144
5.3 NFSV NFSV 3
5 4-17 60
1 NFSV 1,920 640
- 2 (Combined Mode: O-ANFIS O-OII) - 2 (Testing Mode: Adaptive Non-Adaptive Learning) - 2 (Input Expanding: Expanded Non-Expanded) - 4 (Prediction Dimension: Close High Low Open) - 2 (Event Filtering: Filtered Non-Filtered) - 10 5.1.3.1 NFSV OCB
ANFIS OII
4 ANFIS OII (5-1) OCB (5-2) m (5-12) 19 2549 5.1.3.1 19 20
145
4
OCB m = 1.45 (Input Offset) 10 4 IRP PTTCH TMB VNT OCB SVR OCB 3 10 OCB OCB 5-11 5-13
3
5-11 NFSV 10
Ustat NFSV type
Input expansion Mode Filtering CLOSE HIGH LOW OPEN
non 1.124 1.182 0.905 0.651 non filter 1.059 0.988 0.849 0.504 non 1.122 1.183 0.896 0.665
Non adapt
filter 1.063 0.98 0.842 0.515 non 1.038 1.089081 0.878619 0.656976 non filter 0.966 0.880991 0.81978 0.487524 non 1.054 1.091477 0.884077 0.664484
O-ANFIS
expand adapt
filter 0.992 0.888284 0.822688 0.50163 non 1.137 1.193482 0.893658 0.655953 non filter 1.079 0.986057 0.83481 0.501861 non 1.119 1.209303 0.881012 0.665776
Non adapt
filter 1.076 1.002403 0.817728 0.515392 non 1.064 1.122794 0.899685 0.672924 non filter 0.998 0.909507 0.858424 0.519339 non 1.027 1.136806 0.868289 0.652106
O-OII
expand adapt
filter 0.962 0.919665 0.808547 0.495949 non 4.311 4.849 3.074 2.053 non filter 4.784 4.717 3.949 2.004 non 2.763 5.34 1.766 1.365
SVR
expand filter 2.96 5.117 2.208 1.281 non 2.101 2.169 1.351 0.963 non filter 2.257 1.992 1.561 0.867 non 1.532 3.094 1.043 0.922
OCB
expand filter 1.538 2.943 1.129 0.812 non 1 1 0.994 0.999 AMA filter 0.906 0.906 0.897 0.907
146
O-ANFIS O-OII SVR OCB O-ANFIS O-OII SVR OCB
NFSV O-OII
AMA NFSV NFSV OCB FC UPL NFSV ( 5.4.5)
5-12 NFSV OCB
Ustat NFSV type
Input expansion
Mode Filtering CLOSE HIGH LOW OPEN
Non 1.037 1.092 0.887 0.66 non
Filter 0.933 0.825 0.777 0.466 Non 1.045 1.112 0.88 0.671
Non adapt
Filter 0.948 0.831 0.774 0.474 Non 1.001 1.05 0.864 0.66
non Filter 0.902 0.772 0.756 0.445 Non 1.009 1.036 0.871 0.662
O-ANFIS
expand adapt
Filter 0.929 0.768 0.76 0.454 Non 1.046 1.119 0.886 0.664
non Filter 0.941 0.832 0.783 0.467 Non 1.034 1.125 0.866 0.667
Non adapt
Filter 0.96 0.847 0.755 0.473 Non 1.001 1.074 0.871 0.671
non Filter 0.908 0.781 0.776 0.483 Non 0.975 1.067 0.86 0.659
O-OII
expand adapt
Filter 0.886 0.775 0.76 0.46 Non 1.091 1.261 1.219 1.071
non Filter 1.141 1.11 1.498 0.969 Non 1.183 1.735 1.058 0.972
SVR
expand Filter 1.173 1.555 1.195 0.854 Non 1.032 1.336 1.152 0.784
non Filter 1.019 1.16 1.349 0.653 Non 1.179 1.138 0.976 0.773
OCB
expand Filter 1.122 0.992 1.008 0.637 Non 1 0.993 0.998 1
AMA
Filter 0.872 0.867 0.877 0.924
2 IRP SET
147
NFSV O-OII 5-22 IRP 4 REC 5-23
5-13 NFSV OCB
Ustat NFSV type
Input expansion
Mode Filtering CLOSE HIGH LOW OPEN
non 1.253 1.316 0.931 0.637 non
filter 1.248 1.233 0.956 0.561 non 1.236 1.291 0.92 0.655
Non adapt
filter 1.234 1.204 0.944 0.578 non 1.095 1.147 0.901 0.653
non filter 1.061 1.044 0.915 0.551 non 1.12 1.175 0.904 0.668
O-ANFIS
expand adapt
filter 1.086 1.069 0.917 0.574 non 1.273 1.305 0.905 0.645
non filter 1.287 1.218 0.912 0.555 non 1.248 1.336 0.904 0.663
Non adapt
filter 1.25 1.235 0.912 0.579 non 1.158 1.196 0.943 0.676
non filter 1.132 1.103 0.983 0.574 non 1.105 1.241 0.881 0.642
O-OII
expand adapt
filter 1.077 1.136 0.881 0.549 non 9.14 10.23 5.856 3.526
non filter 10.25 10.13 7.627 3.555 non 5.132 10.75 2.829 1.953
SVR
expand filter 5.642 10.46 3.726 1.92 non 3.706 3.419 1.648 1.232
non filter 4.114 3.239 1.879 1.187 non 2.061 6.026 1.143 1.146
OCB
expand filter 2.163 5.869 1.31 1.074 non 1 0.995 1 1
AMA
filter 0.957 0.942 0.953 0.967 5-23 NFSV OCB
SVR AOC 4 IRP
SVR OCB SVR NFSV
5-24 SVR OCB
148
SVR NFSV 5-23 OCB SVR
5-22 IRP
5-23 REC IRP
149
5-14 NFSV SVR OCB AMA AMA NFSV 5-11 5-13
5-24 IRP
5-14 IRP CLOSE HIGH LOW OPEN
NFSV AOC mse AOC mse AOC mse AOC Mse Non 0.099 0.02 0.172 0.044 0.084 0.0127 0.058 0.006
Adapt 0.096 0.019 0.195 0.056 0.07 0.0118 0.042 0.004SVR 1.023 1.205 1.748 3.65 0.33 0.1317 0.12 0.021OCB 0.201 0.144 0.83 1.73 0.099 0.0264 0.078 0.014AMA 0.066 0.011 0.078 0.014 0.079 0.0179 0.084 0.017
NFSV
150
AOC = 0.172 mse = 0.044 AOC = 0.195 mse = 0.056 NFSV BRG (Benefit Retrain Graph)
BRG 5-25 9 BRG 3 3 G-OII NFSV ( 5.4.5)
5-25 BRG IRP
BRG BRT 0.70909 0.70732 0.78761 1 2 3 BRT (Stem) NFSV
151
BRG 2 3 BRT 0.80357 0.80735
IRP G-OII GC FC 4 5-26 5-27 NFSV
5-26 NFSV IRP
() ()
() ()
152
G-OII 5-26() () 5-27() () G-OII 5-27
5-27 NFSV IRP 5-26() 5-27()
G-OII 5-27() 5-26() 5-27()
() ()
() ()
153
GC FC G-OII UPL GC & FC Filter 5-26 5-27 G-OII UPL GC FC
SET 5-28 5-29
5-28 NFSV SET
() ()
() ()
154
5-29 NFSV SET NFSV
G-OII SET 19 2549
SET 108.41 730.55 622.14 NFSV 5-28 5-29
NFSV
() ()
() ()
155
5.4
AMA 1 2 1 2 AMA 1 NFSV
SVR OCB SVR SVR OCB
SVR OCB OCB SVR OCB
ANFIS OII 0 1 NFSV 04 0.5
SVR OCB ANFIS OII 6 O-ANFIS O-OII
156
SVR OCB NFSV
NFSV 3 5 4 60
O-ANFIS O-OII 10 1,920 640
Nave (Exponential Smoothing) AMA Ustat 1 Nave 1 Nave 5-11 5-13
AMA AMA NFSV Nave NFSV OCB FC UPL
NFSV O-OII
6
SVR
ANFIS NFSV
6.1 SVR
SVR
OCB SVR SVR OCB SVR
6.2 ANFIS
ANFIS
OII ANFIS
158
6.3 NFSV SVR ANFIS
OCB OCB
NFSV O-OII
AMA NFSV Nave NFSV OCB FC UPL
6.4
OCB OII SVR ANFIS OCB SVR NFSV OCB OII OCB OCB NFSV NFSV
159
NFSV 5-28 5-29
6.5
NFSV
OCB OCB
NFSV ANFIS (Genetic Algorithm) [45, 46]
(Multi-Level Laws) [47] (Long-Memory Effects) (Short-Term Effects)
160
(Wavelet) [48]
2 [49, 50] ANFIS
2
[51]
NFSV
[52] (Lower Upper Bound) [53]
161
(Reverse Strategy) [54]
(Negative Serial Correlation) [55] [56] [57, 58]
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