金融时间序列预测中的神经网络方法

概述了神经网络方法在金融时间序列预测应用中所面临的有关问题,给出了解决方法;针对有关模型和算法作了计算模拟与分析,得到了一些可供今后研究参考的经验结果;讨论了金融时间序列预测中主要的神经网络模型,如多层前馈网络、径向基函数网络以及支持向量机网络等.总结了关于模型改进的一些近期研究进展与结果,指出了神经网络用于金融时间序列预测的一些可能的方向.

Neural network methods in financial time series forcasting

Some problems in applications of neural network methods to financial time series forecasting are introduced. Same methods to solve these problems are proposed. Numerical simulations and analysis are performed based on some relevant models and algorithms. Some useful conclusions are obtained. Several neural network models, such as multilayer feadforward models, radial basis function neural networks and support vector machines, used mainly in financial time series forecasting are discussed and analyzed. Recent advances and results of studies on some modified methods are summarized. Some possible topics for the future study applying neural networks to financial time series forecasting are pointed out.