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Financial time series prediction using polynomial pipelined neural networks

机译:使用多项式流水线神经网络的金融时间序列预测

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This paper proposes a novel type of higher-order pipelined neural network: the polynomial pipelined neural network. The proposed network is constructed from a number of higher-order neural networks concatenated with each other to predict highly nonlinear and nonstationary signals based on the engineering concept of divide and conquer. The polynomial pipelined neural network is used to predict the exchange rate between the US dollar and three other currencies. In this application, two sets of experiments are carried out. In the first set, the input data are pre-processed between 0 and 1 and passed to the neural networks as nonstationary data. In the second set of experiments, the nonstationary input signals are transformed into one step relative increase in price. The network demonstrates more accurate forecasting and an improvement in the signal to noise ratio over a number of benchmarked neural networks.
机译:本文提出了一种新型的高阶流水线神经网络:多项式流水线神经网络。所提出的网络是由许多相互连接的高阶神经网络构成的,基于分而治之的工程概念,可以预测高度非线性和不稳定的信号。多项式流水线神经网络用于预测美元与其他三种货币之间的汇率。在本申请中,进行了两组实验。在第一组中,输入数据在0到1之间进行预处理,并作为非平稳数据传递到神经网络。在第二组实验中,非平稳输入信号被转换为价格的相对增加一级。与许多基准神经网络相比,该网络展示了更准确的预测和信噪比的改善。

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