Forecasting and recombining time-series components by using neural networks
- 1 March 2003
- journal article
- research article
- Published by Taylor & Francis in Journal of the Operational Research Society
- Vol. 54 (3), 307-317
- https://doi.org/10.1057/palgrave.jors.2601523
Abstract
Journal of the Operational Research Society (JORS) is the world's longest established OR journal and an official journal of The OR Society. It is the aim of the Journal to present papers which are relevant to practitioners, researchers, teachers, students and consumers of operational research, and which cover the theory, practice, history or methodology of OR.Keywords
This publication has 11 references indexed in Scilit:
- Time Series Prediction With Genetic‐Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical ModelsComputational Intelligence, 1999
- Using Feature Construction to Improve the Performance of Neural NetworksManagement Science, 1998
- Neural Network Models for Time Series ForecastsManagement Science, 1996
- Measuring Business Cycles: A Modern PerspectiveThe Review of Economics and Statistics, 1996
- Stacked generalizationNeural Networks, 1992
- Modular Construction of Time-Delay Neural Networks for Speech RecognitionNeural Computation, 1989
- The accuracy of extrapolation (time series) methods: Results of a forecasting competitionJournal of Forecasting, 1982
- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for HeteroskedasticityEconometrica, 1980
- A Simple Test for Heteroscedasticity and Random Coefficient VariationEconometrica, 1979
- TESTING FOR SERIAL CORRELATION IN LEAST SQUARES REGRESSION. IIBiometrika, 1951