Forecasting and recombining time-series components by using neural networks

Journal of the Operational Research Society

Volumn 54, Issue 3, 2003, Pages 307-317

  Hansen, J V ^a   Nelson, R D ^a  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

Author keywords

Forecasting; Neural networks; Time series

Indexed keywords

BACKPROPAGATION; DECISION MAKING; GENETIC ALGORITHMS; TECHNOLOGICAL FORECASTING; TIME SERIES ANALYSIS;

BUSINESS DECISIONS;

NEURAL NETWORKS;

EID: 0037354136     PISSN: 01605682     EISSN: None     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2601523     Document Type: Article

References (22)

1. Burns AF and Mitchell WC (1946). Measuring Business Cycles. National Bureau of Economic Research: New York.
2. Diebold FX and Rudebusch GD (1996). Measuring business cycles: a modern perspective. Rev Econ Stat 78: 67-77.
3. Hill T, O'Connor M and Remus W (1996). Neural network models for time series forecasts. Mngt Sci 42: 1082-1092.
4. Hansen JV, McDonald JB and Nelson RD (1999). Time series prediction with genetic-algorithm designed neural networks: an empirical comparison with modern statistical models. Computl Intell 15: 171-184.
5. Wolpert DH (1992). Stacked generalization. Neural Networks 5: 241-259.
6. Piramuthu S, Ragavan H and Shaw MJ (1998). Using feature construction to improve the performance of neural networks. Mngt Sci 44: 416-430.
7. Atkinson AC (1985). Plots, transformations, and regression. Clarendon Press: Oxford.
8. Shiskin J, Young AH and Musgrave JC (1967). The X-11 variant of the census II seasonal adjustment program (Technical Report #15). US Department of Commerce, Bureau of Economic Analysis: Washington, DC.
9. Newbold P and Bos T (1994). Introductory Business and Economic Forecasting. South-Western Publishing Co.: Cincinnati, OH.
10. Breusch T and Pagan A (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica 47: 1287-1294.
11. White H (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica 48: 817-838.
12. Durbin J and Watson G (1950). Testing for serial correlation in least squares regression - I. Biometrika 37: 409-428.
13. Durbin J and Watson G (1951). Testing for serial correlation in least squares regression - II. Biometrika 38: 159-178.
14. Waibel A (1989). Modular construction of time-delay neural networks for speech recognition. Neural Comput 1: 39-46.
15. Harp S, Smad T and Guha A (1989). Toward the genetic synthesis of neural networks. In: Shaffer D (ed). Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufman: San Mateo, CA.
16. Miller G, Todd P and Hedge S (1989). Designing neural networks using genetic algorithms. In: Shaffer D (ed). Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufman: San Mateo, CA.
17. Hassoun M (1995). Fundamentals of Artificial Neural Networks. MIT Press: Cambridge, MA.
18. Garson GD (1998). Neural Networks: An Introductory Guide for Social Scientists. Sage Publications: London.
19. Box G and Jenkins G (1970). Time Series Analysis: Forecasting and Control. Holden-Day: Holden-day San Francisco.
20. Makridakis S et al (1982). The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecasting 1: 111-153.
21. Stellwagen EA and Goodrich RL (1999). Forecast Pro®, Software Version 4.
22. McDonald JB and Xu Y (1994). Some forecasting applications of partially adaptive estimators of ARIMA models. Econ Lett 45: 155-160.