Forecasting Behavior of Economic Multivariate Time Series with Neural Networks
In this paper,the neural network approach to nonlinear multivariate time series forecasting is investigated.(1)The BDS test is employed to test nonlinear dependence within time series.(2)A statistical method based on fractal dimension is designed for testing nonlinear dependence between time series.(3)A learning algorithm for neural networks based on modified genetic algorithm is proposed.The methods of this paper have been used in forecasting the Shanghai Composite Index of Chinese Restate and work very well.
Neural network Modified genetic algorithms BDS test Fractal dimension Nonlinear Time series Economic forecasting Statistical inference Real estate price index
Chongjun Fan
School of Management,University of Shanghai for Science and Technology,Shanghai 200093,China
国际会议
2008 International Conference on System Management(2008年系统管理学术研讨会)(2008 CSM)
上海
英文
37-41
2008-05-30(万方平台首次上网日期,不代表论文的发表时间)