análisis intelectual de series temporales. yarushkina n.g
DESCRIPTION
Texto de enseñanza en ruso sobre minería de datos. Ulianovsk. 2010TRANSCRIPT
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........................................................................................................... 6 .............................................. 11 1. ....................... 13 ............................................................................................................... 13 1.1. .................................... 14 ......................................................................................... 20 1.2. ............................................................................ 20 ......................................................................................... 26 1.3. .................................................. 27 ......................................................................................... 31 1.4. .................................................................................... 31 ......................................................................................... 34 1.5. .. 35 ......................................................................................... 40 1.6. ............. 41 ......................................................................................... 44 ................................................................................................................. 44 ................................................................................ 45 2. ................................................................. 58 ............................................................................................................... 58 2.1. ..................................................................................... 59 ......................................................................................... 64 2.2. .................................................................... 65 ......................................................................................... 67 2.3. ............................................................................................ 67 ......................................................................................... 70 2.4. ........................................................................ 71 ......................................................................................... 77 2.5. ............................... 77 ......................................................................................... 81 2.6. ............................... 82 ......................................................................................... 95 ................................................................................................................. 96 ................................................................................ 98
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3. ............................................. 100 .............................................................................................................. 100 3.1. ................ 101 ........................................................................................ 111 ............................................................................... 112 3.2. ................... 113 ........................................................................................ 122 ............................................................................... 122 3.3. ........................... 123 ........................................................................................ 143 ............................................................................... 143 3.4. ...................................................... 145 ........................................................................................ 152 ............................................................................... 152 3.5. ................................................ 154 ........................................................................................ 161 ................................................................................................................ 162 ............................................................................... 165 4. ................................................................ 168 .............................................................................................................. 168 4.1. ACL- . 170 ........................................................................................ 193 4.2. ......................................................... 194 ........................................................................................ 202 4.3. .............. 202 ........................................................................................ 209 4.4. FT- ................................................................................... 209 ........................................................................................ 210 4.5. - ..................................... 210 ........................................................................................ 217 ............................................................................... 217 5. - .............. 219 .............................................................................................................. 219 5.1. - .. 219 ........................................................................................ 224 5.2. ................................ 225 ........................................................................................ 233
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5.3. ............................. 234 ........................................................................................ 236 5.4. ........................................................................................ 236 ........................................................................................ 238 5.5. ............................ 239 ........................................................................................ 246 5.6. ...................................................................................... 246 ........................................................................................ 249 5.7. ................................ 250 ........................................................................................ 270 5.8. ....................................................... 270 ........................................................................................ 277 5.9. ............................................ 278 ........................................................................................ 279 5.10. ............................... 279 ........................................................................................ 285 ................................................................................................................ 285 ............................................................................... 286 6. ....................................................... 289 .............................................................................................................. 289 6.1. FuzzyTend ............................................... 289 6.2. .................................................................. 300 ........................................................................................ 313 ................................................................................................................ 313 ............................................................................... 314 .................................................................................................. 316 ...................................................................................................... 317
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, , - - -, , - - .
, - , - Data Mining, Time Series Data Mining (TSDM). - Time Series Data Mining , -, , , , -, - , , -, .
Time Series Data Mining , (), - . - , , , - - - -. . , . , , Data Mining : , , , , -, , , , - .
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, - : . , . , . , . , . , . , . . - . , . , . .
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: , , .
Time Series Data Minig, : , , , . - , . - - -.
. - - ACL-. - - ACL- . . , F- - , - Time Series Data Minig.
- - - (Time Series Data Minig): , , , , , , . - , -
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, , - . -, - .
- . - - , - - .
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ACL- (Absolute&Comparative Linguistic) -
. CICO CICO (Crisp-Input and
CrispOutput).
CiFo CIFO (Crisp-Inputs and Fuzzy-Outputs). CWP (computing with words and perceptions
CWP).
DM Data Mining (DM) .
FARIMA - (FARIMA)
FAT Fuzzy Approximation Theorem, - , - .
FiFo FIFO (Fuzzy-Inputs and Fuzzy-Outputs).
FLSRA , FLSRA (Fuzzy least-square regression analysis).
GCL : Generalized Constraint Language. GTU (Generalized Theory
of Uncertainty.
IFSA , , Interna-tional Fuzzy Systems Association.
NL , .
TPM Theory of Precisiation of Meaning (TPM). TSDM Time Series Data
Mining (TSDM).
.
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. (ARIMA) -
. . . . . . . . . . , . . . . . . . . . . (MSE) . . .
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44
- , - , (-) , - . - , , , , , , , .
1. [, 2008 ] , . . -
/ . // (-2008): (. , 27-29 , 2008 .). 2. : , 2008. . 3-9.
2. [, 2008 ] , . . / . - // (-2008) : - (. , 27-29 , 2008 .). . 2. - : , 2008. . 9-23.
3. [ ., 1986] - / . . , . . , . . . ; . . . . . : . . . .-. ., 1986. 312 .
-
45
4. [ ., 2008] , . . - / . . , . . , . . // - -2008 (28 - 3 , 2008 ., . , ) : -. .1. . : , 2008. . 269-280.
5. [ ., 2008] , . . CAD/CAM/CAT- / . . , . . // - - (AIS08) (CAD-2008). 4- . . : , 2008. . 3. . 312-314.
6. [ ., 1990] , . . - / . . , . . , . . // . . . . . 29. . : , 1990. . 127-201.
7. [ ., 2008] , . . - / . . , . . - // - - (AIS08) (CAD-2008). 4- . . : , 2008. . 1. . 95-100.
8. [ ., 2003] , . . - : . / . . , . . , . . ; . . . . . : , 2003. 368 .
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46
(, 20-30 2009 .). .2. . : , 2009. . 785-799.
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15. [ ., 2007 ] , . . - / . . , . . // . . 2. 2007. 1.
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18. [ ., 2002] , . . - / . . , . . , . . . . : , 2002.
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21. [ ., 2007] , . . / . . , . . , . . . . : -, 2007. 284 .
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24. [ ., 2008] , . . / . . , . . // -
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-
57
2.
- , -. - , - - . - , , - , -. , - , , - () . - [Zadeh, 1965; , 1974; , 2007; , 1986; , 1989; , 1999; , 2004; , 2006; , 2006; ., 2007; ., 2007].
, , - (), , :
1. , - , - , . - , , - . , - .
-
58
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, . [Zadeh, 1965] - , - , , - . , - , , .
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. Toward a Computational Theory of Precisiation of Meaning Based on Fuzzy Logic The Concept of Cointensive Precisiation ,
. , .
Order Structure, Topology and Fuzzy Sets ,
. From Natural Language to Formalized Language and Back
. On the Links between Probability and Possibility Theories
. Knowledge-based Clustering for Human-Centric Systems, ,
2.2
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.
Fuzzy Logic as the Logic of Natural Languages
. Computing with Words and Granules
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. Computing with words, usuality qualification and linguistic quantifiers: tools for human-centric computing , :
. Dynamic and Distributed (D2) Fuzzy Modeling
-
82
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. Uncertainty and Fuzziness in Knowledge Discovery from Large Databases
. ( )
OWA Operators for Gene Product Similarity, Clustering, and Knowledge Discovery OWA ,
2.3
IFSA09
. A Unified View of Uncertainty Theories
. Fuzzy Modeling: Fundamentals, Design and Challenges : ,
. Visual Clustering Methods
. Fuzzy and Probabilistic Clustering
. Artificial Neural Networks
. , .
Introduction to Fuzzy Networks
-
83
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. -
Fuzzy Data in Statistics: Formalization and Main Problems :
. Soft Computing for Sensor and Algorithm Fusion
. Robust Statistics
. An Axiomatic Approach to the Notion of Rational Preference Structures
. Feature Selection
2.4
IFSA- EUSFLAT09
. , .
Casual Communication with Robots using Speech Recognition Module , -
. Fuzzy Logic in Machine Learning
. Capacities and the Choquet integral in decision making: a survey of funda-mental concepts and recent advances : -
. Fuzzy Systems, Choice Paradoxes and Optimal Committees ,
-
84
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-
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86
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IFSA09 IFSA97 IFSA03, IFSA05 IFSA07 - , . 2.5.
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- c
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1. 33/9 20/8 16/5 20/9 35/11 = 1.1.
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-
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-
93
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SA07 IFSA09 . IFSA07 14 128 , IFSA09 - 32 (240 ). , ( , , Data Mining, - Web), (. 2.6).
IFSA05 Data Mining (DM) . , , , .
1. International Fuzzy Systems Association (IFSA)? 2. . ? 3. . IFSA09. 4. . -
IFSA05.
-
94
5. . IFSA05 IFSA07.
6. . IFSA05, . IFSA07. . IFSA07.
7. . , IFSA09.
8. . - IFSA09.
9. . : - . , - IFSA09.
10. .
, - -. - - : 1. ; 2. ; 3. ; 4. , -
; 5. .
-
95
2.6 IFSA09
Recent advances in Evolving Fuzzy Systems -
E. Lughofer, D. Filev, P. Angelov
Advances in Soft Computing Applied to Databases and Information Systems ,
P. Bosc, A. Hadjali, O. Pivert
Transforms, Time Series and Other Applications ,
I. Perfilieva, V. Novak, N. Yarushkina
Fuzzy and Possibilistic Optimization
M. Inuiguchi, W. A. Lodwick, M. Luhandjula
Fuzzy Differential Equations
Y. Chalco-Cano, W. A. Lodwick
Soft-computing for Web 2.0 and Semantic Web Web 2.0 Web
R. Yager, M. Reformat
Solvability of Fuzzy Relation Equations and Fuzzy Inter-polation
I. Perfilieva, M. Stepnicka
Aggregation Operators
H. Bustince, T. Calvo, R. Mesiar
Fuzzy Sets in Computational Biology
U. Bodenhofer, E. Huellermeier, F. Klawonn
Mathematical Fuzzy Logic
P. Cintula, C. Noguera
Machine Learning and Data Mining
P. Angelov, E. Huellermeier, F. Klawonn, D. Sanchez
Type-2 Fuzzy Logic, Advances and Applications 2,
A. Celikyilmaz, I. B. Turksen
Inter-relation Between Interval and Fuzzy Techniques
V. Kreinovich
Advances in Soft Computing for Spatiotemporal Informa-tion Systems -
G. De Tr, R. Ribeiro, J. Dujmovic
Interpretability of Fuzzy systems: Theory and Applications :
J. M. Alonso, L. Magdalena
Computing With Words, Actions and Perceptions ,
S. Guadarrama
New trends in Fuzzy Reasoning of Robotic Systems
P. J. Sequeira Gonalves, L. F. Mendona
-
96
. 2.6 Fuzzy Numbers and Fuzzy Arithmetic
P. Grzegorzewski, L. Stefanini
Intuitionistic Fuzzy Sets
E. Szmidt, J. Kacprzyk
New Advances on Genetic Fuzzy Systems
Y. Nojima, R. Alcal
Measures and Integrals
M. Grabisch
Fuzzy Geographical Information
C. C. Fonte, J. Santos, M. Caetano, L. Gonalves
Soft Computing in Image Processing and Computer Vision
Soft Computing in Image Processing and Computer Vision
Models and Fuzzy Arithmetic in Economics and Business
M. L. Guerra and L. Stefanini
Soft Computing in Finance
R. J. Almeida, M. Lovric, V. Milea
Topics in Decision-Making Using Fuzzy Sets ,
D.Ralescu
Decision Making in Fuzzy Environments
M. T. Lamata, D. Pelta
Soft Computing in Medical Imaging
I. K. Vlachos, G. Schaefer
Medical Concepts in Soft Computing
C. Schuh, R. Seising
Philosophical, Sociological and Economical Thinking ,
E. Trilla, R. Seising, H. Nurmi
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- , - .
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-
-
122
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3.3.1.
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-
123
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-
125
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-
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F- - [ ., 2009].
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137
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(k) = (x1(k), x2(k), , xm(k)) = (x1(t1k), x2(t2k), , xm(tmk)), xj(k), mj ,1 yi(k) tk.
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21
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-
139
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)( iJr J .
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(
N)
(N)
(N
)
1 1
0 0 0 0 0
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3.2 , -
- 1, 3, 6 1, 2, 5, 7.
-
140
1. . 2. . 3. ? 4. . 5.
. 6. ?
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20. [, 2008] , . . , - / . . [ : http://www.exponenta.ru/educat/news/degtyarev/paper2.pdf; 30.12.2009].
21. [ ., 2007] , . . - / . . , . . -, . . // , , , 2007. 12. . 12-15.
22. [ ., 2009] , . . F- - / . . , . . , . . // 2009 : -. : , 2009. . 459-461.
23. [, 2008] , . - / . , . , . . // . 2008. 4 . . 65-68.
3.4.
, , , , - , , . , - , - . -
-
143
, , - -
3.4.1. -
,
, - - . , (Least-square), , - . - -, . ( -, ). - .
, 1982 . , - . , , .
-
-
144
. (vagueness) () ( ).
- .
, -, , - , ( -) .
(. 3.3).
PossibilisticFuzzyRegressionModel
Fuzzyleast-squareregressionanalysis
. 3.3.
, (Possibilistic Fuzzy Regression Model) [Tanaka, 1982]. , FLSRA (Fuzzy least-square regression analysis) [Diamond, 1988; Celmi, 1987].
, , , , .
, - -
-
145
, , - , -.
, .
: mjxxy jnjj ,...,1,,..., 1 , :
nn xAxAAY~
1~
1~0
~... ,
nissaA RiLicii ,...,1),,,( ; cia ~iA ;
Ri
Li ss , . : 1. jy
~jY mjhyY jj ,...,1,)(~ , h -, , .
2. . - . , - mjxxy jnjj ,...,1,,..., 1 :
),,...( 0 cnc aa ),,...( 0 LnLL sss ),,...( 0 RnRR sss
n
I
m
jij
Ri
Li
LRL xsssssmZ1 1
000 )( .
, - . - (MSE) :
-
146
n
jjj Ydefym
MSE1
2)]([1 ,
)( jYdef . , -
: CICO (Crisp-Input and CrispOutput), - FIFO (Fuzzy-Inputs and Fuzzy-Outputs) CIFO (Crisp-Inputs and Fuzzy-Outputs) [D'Urso, 2003; Hojati, 2005; Bisserier, 2009].
[, 1997; Bardossy, 1990; Sabic, 1991], , - . [, 2000] , - 40% - .
ARIMA -
, . - (FARIMA) [Tsen-ga, 2001]. , - - ARIMA - . FARIMA - ARIMA -.
[Khashei, 2008] , - ARIMA
-
147
- . , ARIMA + + - .
3.4.2. - , -
, - [, 2007; , 2008].
, [Huarng, 2006; Yu, 2008] .
[Alizadeh, 2009] - - . - . - : , , . - .
[Kuo, 2001] . [, 2006] - - , , [ ., 2007; , 2008]
-
148
- .
() , -
- -, - [, 2004].
- , .
- , - . - , - . , , : -, , - .
ANFIS TSK. , - . - -. [, 2004; ., 2007; , 2007; ., 2007].
1.
?
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149
2. ?
3. -?
4. ARIMA ? 5. ?
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16. [ ., 2007] , . . / . . , . . , . . . . : -, 2007. 284 .
17. [, 2006] , . . - / . . // - , . 2006. 7(20). . 142-146.
18. [, 2000] , . . / . . // . . . 308. . : , 2000. 220 .
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151
19. [, 2008] , . . / . . // - . 2008. 3.
20. [, 1997] , . . / . . // . 1997. 11. . 27-32.
21. [, 2004] , . . - : . / . . . . : -, 2004. 320 .
22. [, 2007] , . . - / . . , . . , . . // - . 2007. 4. . 15-19.
3.5.
3.5.1. -
. ,
, - - , .
, . () - . .
[, 2009]. -
-
152
() - k X.
1,...,2,1),( kniwkxW i k- X. ,
iw . -, s : ),...,2,1( siCi .
siai ,...2,1 . -
mjjjaaaxD ,...,,)(
21 .
, , . - , , - - .
- , . - . , , - [, 2009].
, (computing with words and perceptions CWP) [Zadeh, 2001; Batyrshin, 2004].
() - , . . . (attribute-based) - .
-
153
- (generalized constraint) [Zadeh, 2006]. -
X isr R,
X ; r ; R () . : X n- , X= (X1, , Xn); X ; X : X=f(Y); X X/Y; X , , X= Location (Residence(Carol)); X X: Y isr R; X G[A]: (Name1, , Namen), -
Namei, i =1, , n, Ai. X isr R: r: = : X=R, X is=R; r: : X R; r: : X R; r: blank : X is R, R
X; r: v : X isv R, R X; r: p : X isp R, R -
X; r: bm : X R is bm;
-
154
r: rs : X isrs R, R - X;
r: fg : X isfg R, X R ; r: u ( usually): X isu R
(X is R); r: g : X isg R , R
.
: Generalized Constraint Language (GCL). -
. : 1) ; 2) ; 3) ; 4) . -
-: . (computing with words and perceptions CWP) - : ( perception) - . - (generalized constraints). , .
3.5.2. (Time Series Data Mining)
, Data Mining,
-
155
Time Series Data Mining (TSDM).
, - .
Data Mining , . . (CWP) - , . Data Mining , - , , . , - .
Data Mining - , [Batyrshin, 2007]:
1) [Graves, 2009]; 2) [Giove, 2009]; 3)
[Herbst, 2009]; 4)
; 5) (summarization) ,
[Kacprzyk, 2009];
6) , ; 7) ; 8) ;
-
156
9) , - .
- : -, . . : , , , . . - -- , : .
TSDM , [, 2007]:
1) ;
2) - , - .
- ( - ).
- [, 2007]: 1) -
; 2)
; 3) -
( ); 4) -
.
-
157
[, 2007] - , . - : - - . - . - .
- , :
)),((&)),((),( qttttytt bababa .
[ta, tb] q . - , -. , , . - , .
CWP - [, 2009]:
(precisiation) , ; (gene-
ralized constraints);
; -
(generalized constraints);
.
-
158
Time Series Data Mining :
1) [, 2004];
2) : If trend is F then next point is Y [, 2004];
3) - ;
4) [Yu, 2005];
5) [Batyrshin, 2004].
1. . 2. . 3. Data
Mining?
4. Data Mining? 5. - . 6. Time Series Data Mining.
- , 3, , :
1. . -
, . , ,
-
159
, - .
2. . , -
, - . , . .
3. . :
, - , , - () , , , -.
4. ().
, - . : - . - , , , - , . . - .
-
160
3.3 / .
, - , , .
, - , - , , .
2004 . . [, 2004] , . , - , .
3.3
/ ( )
/
1. - 2. 3.
( )
/
1. 2. 3.
( )
/ . / / - /
1. 2. 3.
-
161
. 3.3 - ( )
/
1. - 2. 3.
- ( )
/