A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters

Applied Intelligence

Volumn 34, Issue 3, 2011, Pages 386-393

  Aznarte, José Luis ^a   Molina, Daniel ^b   Sánchez, Ana M ^c   Benítez, José M ^c  

^a UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

^b UNIVERSITY OF CÁDIZ   (Spain)

^c UNIVERSITY OF GRANADA   (Spain)

Author keywords

Diagnostic checking; Fuzzy rule based systems; Heteroscedasticity; Residuals

Indexed keywords

DIAGNOSTIC CHECKING; FUZZY RULE-BASED SYSTEMS; HETEROSCEDASTICITY; HOMOSCEDASTICITY; RESIDUALS; RULE BASED; SOUND MODELING; TESTING FRAMEWORK; TIME SERIES MODELS;

FUZZY RULES; LAGRANGE MULTIPLIERS; RATING; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 79956111931     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0288-x     Document Type: Conference Paper

References (35)

1. Aznarte JL (2008) Modelling time series through fuzzy rule-based models: a statistical approach. PhD thesis, Universidad de Granada
2. Aznarte JL, Benítez Sánchez JM (2006) On the identifiability of TSK additive fuzzy rule-based models. In: Soft methods for integrated uncertainty modelling. Advances in soft computing, vol 6. Springer, Berlin, pp 79-86
3. JL Aznarte JM Benítez 2010 Equivalences between neural-autoregressive time series models and fuzzy systems IEEE Trans Neural Netw 21 9 1434 1444 10.1109/TNN.2010.2060209 10.1109/TNN.2010.2060209
4. JL Aznarte JM Benítez JL Castro 2007 Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences Fuzzy Sets Syst 158 24 2734 2745 10.1016/j.fss.2007.03.021 1121.62074 10.1016/j.fss.2007.03.021 (Pubitemid 47513202)
5. JL Aznarte JM Benítez D Nieto-Lugilde C de Linares Fernández C Díaz de la Guardia F Alba Sánchez 2007 Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models Expert Syst Appl 32 4 1218 1225 10.1016/j.eswa.2006.02.011 10.1016/j.eswa.2006.02.011 (Pubitemid 44821809)
6. JL Aznarte MC Medeiros JM Benítez 2010 Linearity testing for fuzzy rule-based models Fuzzy Sets Syst 161 13 1836 1851 1191.62144 10.1016/j.fss.2010.01.005
7. JL Aznarte MC Medeiros JM Benítez 2010 Testing for remaining autocorrelation of the residuals in the framework of fuzzy rule-based time series modelling Int J Uncertain Fuzziness Knowl-Based Syst 18 4 371 387 1194.62098 10.1142/S021848851000660X
8. A Bárdossy L Duckstein 1995 Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems Systems engineering series CRC Press Boca Raton 0857.92001 http://books.google.es/books? id=YkB-wfN7GBkC
9. Bárdossy A, Duckstein L (1995) Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. Systems engineering series. CRC Press, Boca Raton. http://books.google.es/books?id=YkB- wfN7GBkC
10. Barreto H, Howland F (2005) Introductory econometrics: using Monte Carlo simulation with Microsoft excel. Cambridge University Press, Cambridge
11. M Benrejeb A Sakly KB Othman P Borne 2008 Choice of conjunctive operator of tsk fuzzy systems and stability domain study Math Comput Simul 76 5-6 410 421 10.1016/j.matcom.2007.04.003 2378425 1134.93031 10.1016/j.matcom.2007.04.003 http://www.sciencedirect.com/science/article/B6V0T-4NFH0BH-2/2/ 162d0452aebb66d4e95da94e1e1e842b. Mathematical aspects of modelling and control (Pubitemid 350251073)
12. H Byun K Lee 2005 A decision support system for the selection of a rapid prototyping process using teh modified topsis method Int J Adv Manuf Technol 26 11-12 1338 1347 10.1007/s00170-004-2099-2 (Pubitemid 41539553)
13. E Cárdenas J Castillo O Cordón F Herrera A Peregrín 1995 Applicability of t-norms in fuzzy control Busefal 61 28 36
14. Cordón O, Herrera F, Peregrin A (1995) T-norms versus implication functions as implication operators in fuzzy control. In: Proceedings of the IFSA 1995 conference, Sao Paulo, Brazil, pp 501-504
15. Driankov D, Hellendoorn H, Reinfrank M (1996) An introduction to fuzzy control, 2nd edn. Springer, Berlin
16. JS Jang 1993 ANFIS: Adaptive-network-based fuzzy inference system IEEE Trans Syst Man Cibern 23 3 665 685 1132413 10.1109/21.256541
17. JS Jang CT Sun 1993 Predicting chaotic time series with fuzzy if-then rules Second IEEE international conference on fuzzy systems 1993 2 1079 1084 10.1109/FUZZY.1993.327364 10.1109/FUZZY.1993.327364
18. Jang JS, Sun CT (1993) Predicting chaotic time series with fuzzy if-then rules. In: Second IEEE international conference on fuzzy systems, 1993, vol 2, pp 1079-1084. doi: 10.1109/FUZZY.1993.327364
19. R John P Innocent 2005 Modeling uncertainty in clinical diagnosis using fuzzy logic IEEE Trans Syst Man Cybern, Part B, Cybern 35 6 1340 1350 10.1109/TSMCB.2005.855588 10.1109/TSMCB.2005.855588 (Pubitemid 41800434)
20. J Kim N Kasabov 1999 HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems Neural Netw 12 1301 1319 10.1016/S0893-6080(99)00067-2 (Pubitemid 29475159)
21. I Lee B Kosko WF Anderson 2005 Modeling gunshot bruises in soft body armor with an adaptive fuzzy system IEEE Trans Syst Man Cybern, Part B, Cybern 35 6 1374 1390 10.1109/TSMCB.2005.855585 (Pubitemid 41800437)
22. L Maguire B Roche T McGuinnity L McDaid 1998 Predicting a chaotic time series using a fuzzy neural network Inf Sci 112 1-4 125 136 0953.68590 10.1016/S0020-0255(98)10026-9 (Pubitemid 128649631)
23. M Medeiros A Veiga 2003 Diagnostic checking in a flexible nonlinear time series model J Time Ser Anal 24 461 482 1997124 1036.62086 10.1111/1467-9892. 00316 (Pubitemid 37261114)
24. M Medeiros A Veiga 2005 A flexible coefficient smooth transition time series model IEEE Trans Neural Netw 16 1 97 113 10.1109/TNN.2004.836246 (Pubitemid 40241913)
25. D Nauck R Kruse 1999 Neuro-fuzzy systems for function approximation Fuzzy Sets Syst 101 2 261 271 10.1016/S0165-0114(98)00169-9 1676919 0937.93029 10.1016/S0165-0114(98)00169-9 (Pubitemid 129496792)
26. J Nie 1997 Nonlinear time-series forecasting: a fuzzy-neural approach Neurocomputing 16 1 63 76 10.1016/S0925-2312(97)00019-2 (Pubitemid 27286292)
27. Palit AK, Popovic D (2005) Computational intelligence in time series forecasting: theory and engineering applications (Advances in industrial control). Springer, New York
28. M Suarez-Farinas CE Pedreira MC Medeiros 2004 Local global neural networks: a new approach for nonlinear time series modeling J Am Stat Assoc 99 1092 1107 2109498 1055.62106 10.1198/016214504000001691 http://ideas.repec.org/ a/bes/jnlasa/v99y2004p1092-1107.html (Pubitemid 39665954)
29. M Sugeno G Kang 1988 Structure identification of fuzzy model Fuzzy Sets Syst 28 15 33 961132 0652.93010 10.1016/0165-0114(88)90113-3
30. T Takagi M Sugeno 1985 Fuzzy identification of systems and its applications to modeling and control IEEE Trans Syst Man Cybern 15 116 132 0576.93021
31. T Teräsvirta 1994 Specification, estimation and evaluation of smooth transition autoregresive models J Am Stat Assoc 89 208 218 10.2307/2291217
32. Tong H (1978) On a threshold model. In: Pattern recognition and signal processing
33. CF Vieira LB Palma RN da Silva 2005 Robust fault diagnosis approach using analytical and knowledge based techniques applied to a water tank system Int J Eng Intell Syst Electr Eng Commun 13 4 237 244
34. LX Wang 1992 Fuzzy systems are universal approximators IEEE Trans Syst Man Cybern SMC7 10 1163 1170
35. L Zadeh 1965 Fuzzy sets Inf Control 3 8 338 353 219427 10.1016/S0019-9958(65)90241-X