메뉴 건너뛰기




Volumn 32, Issue , 2005, Pages 151-162

A genetic programming system for time series prediction and its application to el niño forecast

Author keywords

Genetic Programming; Time series forecasting

Indexed keywords


EID: 68049122277     PISSN: 16153871     EISSN: 18600794     Source Type: Book Series    
DOI: 10.1007/3-540-32400-3_12     Document Type: Article
Times cited : (14)

References (32)
  • 1
    • 85120032357 scopus 로고
    • Time series prediction: Forecasting the future and understanding the past, Addison-Wesley, Reading
    • Google Scholar
    • 1.Weigend A, Gershenfeld N (eds) (1994). Time series prediction: forecasting the future and understanding the past, Addison-Wesley, Reading, Massachussetts. Google Scholar
    • (1994) Massachussetts
  • 2
    • 85120080232 scopus 로고    scopus 로고
    • Tong H (1990). Nonlinear time series: a dynamical system approach. Oxford University Press. Google Scholar
    • 2.Tong H (1990). Nonlinear time series: a dynamical system approach. Oxford University Press. Google Scholar
  • 3
    • 85120079465 scopus 로고    scopus 로고
    • Box G, Jenkins G, Reinsel G (1994). Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs, New Jersey. Google Scholar
    • 3.Box G, Jenkins G, Reinsel G (1994). Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs, New Jersey. Google Scholar
  • 4
    • 85120058219 scopus 로고    scopus 로고
    • Friedman J (1991). Multivariate adaptive regression splines. Annals of Statistics 19:1–142. zbMATHMathSciNetGoogle Scholar
    • 4.Friedman J (1991). Multivariate adaptive regression splines. Annals of Statistics 19:1–142. zbMATHMathSciNetGoogle Scholar
  • 5
    • 0002291616 scopus 로고
    • Neural net for temporal sequence processing
    • In: Weigend A, Gershenfeld N (eds), Addison-Wesley, Reading, Massachussetts. Google Scholar
    • 5.Mozer N (1993). Neural net for temporal sequence processing. In: Weigend A, Gershenfeld N (eds) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, Massachussetts. Google Scholar
    • (1993) Time Series Prediction: Forecasting the Future and Understanding the Past
  • 6
    • 85120065640 scopus 로고    scopus 로고
    • Tsoi A, Back A (1994). Locally recurrent globally feedforward networks: a critical review of architectures. IEEE Trans. on Neural Networks 2:229–239. CrossRefGoogle Scholar
    • 6.Tsoi A, Back A (1994). Locally recurrent globally feedforward networks: a critical review of architectures. IEEE Trans. on Neural Networks 2:229–239. CrossRefGoogle Scholar
  • 7
    • 85120071711 scopus 로고    scopus 로고
    • Koskela T, Lehtokangas M, Saarinen J, Kaski K (1996). Time series prediction with multilayer perceptron, FIR and Elman neural networks. In: World Congress on Neural Networks, INNS Press, 491–496. Google Scholar
    • 7.Koskela T, Lehtokangas M, Saarinen J, Kaski K (1996). Time series prediction with multilayer perceptron, FIR and Elman neural networks. In: World Congress on Neural Networks, INNS Press, 491–496. Google Scholar
  • 8
    • 85120051270 scopus 로고    scopus 로고
    • De Falco I, Iazzetta A, Luongo G, Mazzarella A, Tarantino E (2000). The seismicity in the southern tyrrhenian area and its neural forecasting. Pure and Applied Geophysics 157:343–355. CrossRefGoogle Scholar
    • 8.De Falco I, Iazzetta A, Luongo G, Mazzarella A, Tarantino E (2000). The seismicity in the southern tyrrhenian area and its neural forecasting. Pure and Applied Geophysics 157:343–355. CrossRefGoogle Scholar
  • 9
    • 0024861871 scopus 로고
    • Approximations by superpositions of a sigmoidal function
    • zbMATHMathSciNetGoogle Scholar
    • 9.Cybenko G (1989). Approximations by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2:303–314. zbMATHMathSciNetGoogle Scholar
    • (1989) Mathematics of Control, Signals and Systems , vol.2 , pp. 303-314
  • 10
    • 85120033198 scopus 로고    scopus 로고
    • Angeline P J (1998). Evolving predictors for chaotic time series. In: Rogers S, Fogel D, Bezdek J, Bosacchi B (eds) Proceedings of SPIE: Application and Science of Computational Intelligence, Vol. 3390. Bellingham, WA, USA, 170–180. Google Scholar
    • 10.Angeline P J (1998). Evolving predictors for chaotic time series. In: Rogers S, Fogel D, Bezdek J, Bosacchi B (eds) Proceedings of SPIE: Application and Science of Computational Intelligence, Vol. 3390. Bellingham, WA, USA, 170–180. Google Scholar
  • 11
    • 85120062495 scopus 로고    scopus 로고
    • De Falco I, Della Cioppa A, Iazzetta A, Natale P, Tarantino E (1999). Optimizing neural networks for time series prediction. In: Advances in Soft Computing in Engineering Design and Manufacturing. Springer-Verlag, London. Google Scholar
    • 1.De Falco I, Della Cioppa A, Iazzetta A, Natale P, Tarantino E (1999). Optimizing neural networks for time series prediction. In: Advances in Soft Computing in Engineering Design and Manufacturing. Springer-Verlag, London. Google Scholar
  • 12
    • 85120067998 scopus 로고    scopus 로고
    • Lee Ki-Youl, Lee Dong-Wook, Sim Kwee-Bo (2000). Evolutionary neural networks for time series prediction based on L-system and DNA coding method. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1467–1474.Google Scholar
    • 12.Lee Ki-Youl, Lee Dong-Wook, Sim Kwee-Bo (2000). Evolutionary neural networks for time series prediction based on L-system and DNA coding method. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1467–1474.Google Scholar
  • 13
    • 33750236767 scopus 로고
    • Genetic programming. On the programming of computers by means of natural selection
    • Cambridge, MA, USA. Google Scholar
    • 13.Koza J (1992). Genetic programming. On the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA. Google Scholar
    • (1992) MIT Press
  • 14
    • 85120081328 scopus 로고    scopus 로고
    • Iba H (1993) System identification using structured genetic algorithms. In: Forrest S. (Ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California. Morgan Kaufmann, 279–286. Google Scholar
    • 14.Iba H (1993) System identification using structured genetic algorithms. In: Forrest S. (Ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California. Morgan Kaufmann, 279–286. Google Scholar
  • 15
    • 85120068836 scopus 로고    scopus 로고
    • Oakley H (1994) Two scientific applications of genetic programming: stack filters and nonlinear equation fitting to chaotic data. In: Kinnear K (ed.) Advances in Genetic Programming. MIT Press, Cambridge, MA, USA, 369–389. Google Scholar
    • Oakley H (1994) Two scientific applications of genetic programming: stack filters and nonlinear equation fitting to chaotic data. In: Kinnear K (ed.) Advances in Genetic Programming. MIT Press, Cambridge, MA, USA, 369–389. Google Scholar
  • 16
    • 85120053147 scopus 로고    scopus 로고
    • Mulloy B S, Riolo R L, Savit R S (1996). Dynamics of genetic programming and chaotic time series Prediction. In: Koza J R, Goldberg D E, Fogel D B, Riolo R L (eds), Genetic Programming 1996: Proceedings of First Annual Conference, Stanford University, CA, USA, July 28–31, 166–174. Google Scholar
    • Mulloy B S, Riolo R L, Savit R S (1996). Dynamics of genetic programming and chaotic time series Prediction. In: Koza J R, Goldberg D E, Fogel D B, Riolo R L (eds), Genetic Programming 1996: Proceedings of First Annual Conference, Stanford University, CA, USA, July 28–31, 166–174. Google Scholar
  • 17
    • 85120079284 scopus 로고    scopus 로고
    • GP-based heart rate prediction for artificial heart control
    • In: Brave S. and Wu A. S. (Eds), Orlando, Florida, USA, July 13–17, Google Scholar
    • Numata M, Yoshihara I, Yoshizawa M, Abe K (1999). GP-based heart rate prediction for artificial heart control. In: Brave S. and Wu A. S. (Eds) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, July 13–17, 193–197. Google Scholar
    • (1999) Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference , pp. 193-197
  • 18
    • 85120039249 scopus 로고    scopus 로고
    • Iba H, Nikolaev N I (2000). Genetic programming polynomial models of financial data series. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1459–1466. Google Scholar
    • Iba H, Nikolaev N I (2000). Genetic programming polynomial models of financial data series. In: Congress on Evolutionary Computation, La Jolla, California, USA, July 16–19. IEEE Press, 1459–1466. Google Scholar
  • 19
    • 85120069871 scopus 로고    scopus 로고
    • Nikolaev N I, Iba H (2001) Regularization approach to inductive genetic programming. IEEE Trans. on Evolutionary Computation, 4:359–375. CrossRefGoogle Scholar
    • Nikolaev N I, Iba H (2001) Regularization approach to inductive genetic programming. IEEE Trans. on Evolutionary Computation, 4:359–375. CrossRefGoogle Scholar
  • 20
    • 85120039483 scopus 로고    scopus 로고
    • Duan M, Povinelli R J (2001). Estimating stock price predictability using genetic programming. In: Spector L, Goodman E D, Wu A, Langdon W B, Voigt H M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M H, Burke E (eds) Genetic and Evolutionary Computation Conference (GECCO-2001), S. Francisco, California, USA, July 7–11. Morgan Kaufmann, 193–197. Google Scholar
    • Duan M, Povinelli R J (2001). Estimating stock price predictability using genetic programming. In: Spector L, Goodman E D, Wu A, Langdon W B, Voigt H M, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon M H, Burke E (eds) Genetic and Evolutionary Computation Conference (GECCO-2001), S. Francisco, California, USA, July 7–11. Morgan Kaufmann, 193–197. Google Scholar
  • 21
    • 85120055043 scopus 로고
    • Michigan State University, GA Research and Application Group, Google Scholar
    • Zongker D, Punch W (1995). lilgp version 1.0, Lansing, Michigan State University, GA Research and Application Group. Available at: http://isl.cps.msu.edu/GA/software/lil-gp. Google Scholar
    • (1995) Lilgp Version 1.0, Lansing
  • 22
    • 85120089772 scopus 로고    scopus 로고
    • Takens F (1981). Detecting strange attractors in turbulence. In: Lecture Notes in Mathematics. Springer-Verlag, Berlin. Google Scholar
    • Takens F (1981). Detecting strange attractors in turbulence. In: Lecture Notes in Mathematics. Springer-Verlag, Berlin. Google Scholar
  • 23
    • 85120078506 scopus 로고    scopus 로고
    • Jäske H (1996). Prediction of sunspots by genetic programming and their applications. In: Alander J T (ed), Proceedings of Second Nordic Workshp on Genetic Algorithms, Vaasa, Finland, 19–23 August, 79–88.Google Scholar
    • Jäske H (1996). Prediction of sunspots by genetic programming and their applications. In: Alander J T (ed), Proceedings of Second Nordic Workshp on Genetic Algorithms, Vaasa, Finland, 19–23 August, 79–88.Google Scholar
  • 24
    • 85120049748 scopus 로고    scopus 로고
    • Trenberth K E, (1996) El Niño definition. CLIVAR — Exchanges, 3:6–8. Google Scholar
    • Trenberth K E, (1996) El Niño definition. CLIVAR — Exchanges, 3:6–8. Google Scholar
  • 25
    • 0020391175 scopus 로고
    • Variations in tropical sea surface temperature and surface wind fields
    • Google Scholar
    • Rasmusson E M, Carpenter T H (1982) Variations in tropical sea surface temperature and surface wind fields. Associated with the Southern Oscillation/El Niño, Monthly Weather Review, Vol. 110, 354–384. Google Scholar
    • (1982) Associated with the Southern Oscillation/El Niño, Monthly Weather Review , vol.110 , pp. 354-384
  • 26
    • 85120053576 scopus 로고    scopus 로고
    • Trenberth K E (1997), The definition of El Niño. Bullettin of the American Meteorological Soc., Vol. 78, 2771–2777. CrossRefGoogle Scholar
    • Trenberth K E (1997), The definition of El Niño. Bullettin of the American Meteorological Soc., Vol. 78, 2771–2777. CrossRefGoogle Scholar
  • 27
    • 85120033021 scopus 로고    scopus 로고
    • von Storch H, Zwiers F W (1999) Statistical Analysis in Climate Research. Cambridge University Press, 484. Google Scholar
    • von Storch H, Zwiers F W (1999) Statistical Analysis in Climate Research. Cambridge University Press, 484. Google Scholar
  • 28
    • 85120090774 scopus 로고    scopus 로고
    • El Niño 3.4 SST index series. Available at: http://www.cgd.ucar.edu/cas/catalog/climind/Nino_3_3.4_indices.html. Google Scholar
    • El Niño 3.4 SST index series. Available at: http://www.cgd.ucar.edu/cas/catalog/climind/Nino_3_3.4_indices.html. Google Scholar
  • 29
    • 0010387656 scopus 로고    scopus 로고
    • Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset
    • Google Scholar
    • Barnston A G, Glantz M H, He Y (1998) Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset. Bulletin of the American Meteorological Soc., 217–243. Google Scholar
    • (1998) Bulletin of the American Meteorological Soc. , pp. 217-243
  • 30
    • 85120036676 scopus 로고    scopus 로고
    • Unger D, Barnston A, van den Dool H, Kousky V (1996) Consolidated forecasts of tropical pacific SST in Niño 3.4 using one dynamical model and two statistical models. Experimental Long-Lead Bulletin, Vol. 5:1 of the Hardcopy Version. Google Scholar
    • Unger D, Barnston A, van den Dool H, Kousky V (1996) Consolidated forecasts of tropical pacific SST in Niño 3.4 using one dynamical model and two statistical models. Experimental Long-Lead Bulletin, Vol. 5:1 of the Hardcopy Version. Google Scholar
  • 31
    • 85120045349 scopus 로고    scopus 로고
    • Hsieh W (2000) Nonlinear canonical correlation analysis of the tropical pacific climate variability using a neural network approach. Journal of Climate, 14:2528–2539. CrossRefGoogle Scholar
    • Hsieh W (2000) Nonlinear canonical correlation analysis of the tropical pacific climate variability using a neural network approach. Journal of Climate, 14:2528–2539. CrossRefGoogle Scholar
  • 32
    • 85120089676 scopus 로고    scopus 로고
    • Neural network model forecasts of the Niño 3.4 sea surface temperature
    • Google Scholar
    • Tang B, Hsieh W W, Tangang F T (1998) Neural network model forecasts of the Niño 3.4 sea surface temperature. Experimental Long-Lead Forecast Bulletin, Vol. 7:1. Google Scholar
    • (1998) Experimental Long-Lead Forecast Bulletin , vol.7 , Issue.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.