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首页> 外文期刊>IEE Proceedings. Part F >Time series prediction by adaptive networks: a dynamical systems perspective
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Time series prediction by adaptive networks: a dynamical systems perspective

机译：自适应网络的时间序列预测：动力学系统的观点

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摘要

The links between adaptive layered networks, functional interpolation and dynamical systems are considered and applied to the nonlinear predictive analysis of time series. The ability of networks to produce interpolation surfaces to generators of data (i.e. differential equations, iterative maps) is used to analyse a variety of time series. If a network may be trained to approximate a (static) generator of data, the network may be iterated on its own output to produce a time series with the same characteristics as the training waveform. However, since iterated networks are one example of nonlinear dynamical systems, this raises problems of sensitive dependence upon initial conditions leading ultimately to deterministic chaos. An introduction to the relevant concepts is presented and illustrations are provided from simple chaotic maps, nonlinear differential equations, and stock-market prediction. The latter example is included to illustrate the problems which often occur in real-world data due to noise, undersampling, high dimensionality and insufficient data.
机译：考虑了自适应分层网络，功能插值和动力学系统之间的联系，并将其应用于时间序列的非线性预测分析。网络产生到数据生成器（即微分方程，迭代图）的内插曲面的能力用于分析各种时间序列。如果可以训练网络以近似一个（静态）数据生成器，则可以在网络自身的输出上对其进行迭代以生成具有与训练波形相同的特征的时间序列。但是，由于迭代网络是非线性动力学系统的一个示例，因此提出了对初始条件敏感依赖的问题，最终导致确定性混乱。提出了相关概念的介绍，并通​​过简单的混沌图，非线性微分方程和股票市场预测提供了说明。包括后一个示例以说明由于噪声，欠采样，高维数和数据不足而在现实世界数据中经常出现的问题。

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