Paper

: :

: : . : 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


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


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


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


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


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


SVR & OCB Predicting


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


()

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


()

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


()

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


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


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


Non adapt


O-OII

expand adapt


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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