基于进化训练随机迭代神经网络的金融时间序列建模

Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520) Pub Date : 2000-03-26 DOI:10.1109/CIFER.2000.844621

Fernando Niño, G. Hernández, A. Parra

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引用次数: 4

摘要

本文介绍了如何利用具有位置依赖概率的随机迭代神经网络对时间序列进行建模。该模型假设时间序列来自一个具有紧致全局吸引子和在吸引子上支持的物理概率测度的动力系统。同时，采用进化算法训练随机迭代神经网络，对金融时间序列进行建模。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Financial time series modeling with evolutionary trained random iterated neural networks

The paper shows how to model times series by using random iterated neural networks with place-dependent probabilities. The model assumes that the time series comes from a dynamical system which has a compact global attractor and a physical probability measure supported on the attractor. Also, an evolutionary algorithm is used to train a random iterated neural network that models a financial time series.

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来源期刊

Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)

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