Box & Jenkins Model Identification:A Comparison of Methodologies

Main Article Content

Maria Augusta Soares Machado
Reinaldo Castro Souza
Ricardo Tanscheit

Abstract

This paper focuses on a presentation of a comparison of a neuro-fuzzy back propagation network and Forecast automatic model Identification to identify automatically Box & Jenkins non seasonal models.

Recently some combinations of neural networks and fuzzy logic technologies have being used to deal with uncertain and subjective problems. It is concluded on the basis of the obtained results that this type of approach is very powerful to be used.

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

AZOFF, E. M. (1994) Neural Network Time Series Forecasting of Financial Markets. Chicester John Wiley & Sons Ltd., Baffins Lane.

BRAGA, M. J.; BARRETO, J. M.; MACHADO, M. A. (1995) Conceitos da Matemática Nebulosa na Análise de Risco, Artes & Rabiscus.

DHAR, V.; STEIN, R. (1996) Raising Organizational IQ: Strategies for KnowIedge Intensive Decision Support, Prentice Hall.

JANG, J. -S. R.; SUN, C. T. (1997) Mizutani, E., Neuro-Fuzzy and Soft Computing - A Computational Approach to Learning and Machine Intelligence, Prentice Hall Inc., 1997.

LANGAR; ZADEH (Eds.). (1995) Industrial Applications of Fuzzy,Logic and Intelligent Systems, Piscata,Bay, NJ: IEEE Press.

REYNOLDS, B.; STEVENS; MELLICHAMP; SMITH, M. J. E. (1995) Box-JenkinsForecast Model Identification, A.I. Expert June 1995.

SCHWARTZ The Fuzzv Systems Come to Life in Japan,IEEE Expert, v. 5, n. 1, p. 77-78.

SOUZA, C. R.; CAMARGO, M. E. (1996) Análise e Previsão de Séries Temporais: os Modelos ARIMA, Sedigraf.

TSOUKALAS, L. H.; UHRIG, R. E. (1997) Fuzzy and Neural Approaches in Engineering, John Wiley & Sons INC.

VON, A. C. (1995) Fuzzy Logic Applications Langar~ and Zadeh (Eds.), Industrial Applications of Fuzzy,Logic and Intelligent Systems, Piscata,Bay, NJ: IEEE Press.

Most read articles by the same author(s)

فروشگاه اینترنتی