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首页> 外文会议>IASTED International Conference on Financial Engineering and Applications >COMBINING ONTOGENETIC AND PHYLOGENETIC LEARNING: OPTIMIZING FINANCIAL APPLICATIONS THROUGH BIOLOGICALLY-INSPIRED METHODS
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COMBINING ONTOGENETIC AND PHYLOGENETIC LEARNING: OPTIMIZING FINANCIAL APPLICATIONS THROUGH BIOLOGICALLY-INSPIRED METHODS

机译：组合植入和系统发育学习：通过生物启发方法优化金融应用

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In this paper, we propose a biologically inspired methodology to tackle the problem of financial time series using a multi-faceted solution. The paper analyzes how combining ontogenetic and phylogenetic learning, two concepts borrowed from biology, can help optimize financial applications trough biologically-inspired methods. We evaluate the performance of a heterogeneous mixture of neural network algorithms for predicting the exchange-traded fund DIA. A genetic algorithm is utilized to find the best mixture of neural networks, the topology of individual networks in the ensemble, and to determine the features set. The Genetic Algorithm also determines the window size of the input time-series supplied to the individual classifiers in the mixture of experts. The mixtures of neural network experts consist of recurrent back-propagation networks, and Radial Basis Function networks. The application of Genetic Algorithm on the heterogeneous mixture of powerful neural network architectures shows promise for prediction of stock market time series. These highly nonlinear, stochastic and highly non-stationary time series have been found to be notoriously difficult to predict using conventional linear statistical methods.
机译：在本文中，我们提出了一种生物学启发的方法，可以使用多朝向解决方案解决金融时序序列的问题。本文分析了组合的植入和系统发育学习，两种概念从生物学中结合，有助于优化金融应用槽的生物启发方法。我们评估神经网络算法的异构混合物来预测交易所交易基金Dia的性能。遗传算法用于寻找神经网络的最佳混合，集合中的各个网络的拓扑，并确定集合。遗传算法还确定所提供给专家混合的单个分类器的输入时间序列的窗口大小。神经网络专家的混合物由经常性的背部传播网络和径向基函数网络组成。遗传算法在强大的神经网络架构异构混合中的应用显示了股票市场时间序列预测的承诺。已经发现这些高度非线性，随机和高静止时间序列难以使用传统的线性统计方法预测。

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