Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model

Neurocomputing

Volumn 10, Issue 2, 1996, Pages 149-168

  Wedding II, Donald K ^a   Cios, Krzysztof J ^a  

^a The University of Toledo   (United States)

Author keywords

Box Jenkins; Certainty factors; Forecasting; RBF neural networks

Indexed keywords

FORECASTING; MATHEMATICAL MODELS; RELIABILITY; TIME SERIES ANALYSIS;

CERTAINTY FACTORS; RADIAL BASIS FUNCTION NEURAL NETWORKS; TIME SERIES FORECASTING; UNIVARIANT BOX-JENKINS MODEL;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; PRIORITY JOURNAL;

EID: 0030110883     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/0925-2312(95)00021-6     Document Type: Article

References (20)

1. B. Abraham and J. Ledolter, Statistical Methods for Forecasting (John Wiley & Sons, New York, NY, 1983).
2. G.E.P. Box and G.M. Jenkins Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco, CA, 1970).
3. C. Bishop, Improving the generalization properties of radial basis function neural networks, Neural Computat. 3 (1991) 579-588.
4. D.S. Broomhead and D. Lowe, Multivariable functional interpolation and adaptive networks, Complex Syst. 2 (1988) 321-355.
5. B.G. Buchanan and E.H. Shortliffe, Rule-based Expert Systems (Addison-Wesley, Reading, MA, 1985).
6. S. Chen, C.F.N. Cowan and P.M. Grant, Orthogonal least squares learning algorithm for radial basis function network, IEEE Trans Neural Networks (2) (March 1991) 302-309.
7. S. Chen, S.A. Billings, C.F.N. Cowan and P.M. Grant, Non-linear systems identification using radial basis functions, Int. J. Systems Sci. 21 (12) (1990) 2513-2539.
8. CITIBASE: Citibank economic database [machine-readable magnetic data file]; 1946-1993; New York, Citibank, N.A. 1978.
9. V. Kadirkamanathan, M. Niranjan and F. Fallside, Sequential adaption of radial basis function neural networks and its application to time-series prediction, Neural Information Processing Systems 3, R.P. Lippman, J.E. Moody and D.S. Tourefzky, eds. (Morgan Kaufmann, 1991).
10. A.S. Lapedes and R. Farber, Non-linear signal processing using neural networks: Prediction and system modelling, Technical report, Los Alamos National Laboratory; Los Alamos, New Mexico 87545, 1987.
11. Y. Lee, Handwritten digit recognition using K nearest-neighbor, radial-basis function, and back-propagation neural networks, Neural Computat. 3 (3) (1991) 440-449.
12. J.-A. Leonard, M.A. Kramer and L.-H. Unger, Using radial basis functions to approximate a function and its error bounds, IEEE Trans. Neural Networks (4) (July 1992) 624-627.
13. J.A. Leonard, M.-A. Kramer and L.-H. Unger, A neural network architecture that computes its own reliability, Comput. Chem. Eng., 16 (9) (1992) 819-835.
14. Minitab Inc. Minitab Reference Manual Release 7, (Minitab Inc., State College, PA, 1989).
15. J, Moody and C.J. Darken, Fast learning in networks of locally-tuned processing units, Neural Computat. 1 (1989) 281-294.
16. D.E. Rumelhart and J.L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol 1: Foundations (MIT Press, Cambridge, MA, 1986).
17. T.S. Rao and M.M. Gabr, An Introduction to Bispectral Analysis and Bilinear Time Series Models (Springer-Verlag, New York, NY, 1984).
18. H. Tong, Threshold models in non-linear time series analysis, Lecture Notes in Statistics (Springer-Verlag, New York, NY, 1983).
19. A.S. Weigand, B.A. Huberman and D.E. Rumelhart, Predicting the future: A connectionist approach, Int. J. Neural Syst. 1, (1990) 193-210.
20. Y.-f. Wong, How gaussian radial basis functions work, Proc. Int. Joint Conf. Neural Networks, Vol. 2 (July 1991) 133-138.