简单递归神经网络调整的正弦余弦算法用于股市预测

Luka Jovanovic, Nemanja Milutinovic, Masa Gajevic, Jelena O. Krstovic, Tarik A. Rashid, A. Petrovic
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引用次数: 4

摘要

深度人工神经网络最近在时间序列预测文献中得到了广泛的应用。递归神经网络对这类问题具有更高的适用性,这也是为什么这种类型的网络比其他深度神经网络方法被选择的原因。由于使用了大量的参数,这些网络的简单性是相当可观的。这种特性使得深度递归神经网络非常适合于预测问题。不幸的是,为每个特定任务找到循环神经结构是np困难的,因此使用元启发式是合适的。因此,本文提出的研究解决了用正弦余弦算法调整简单递归神经网络的股票市场预测问题。将该方法的性能与其他元启发式方法进行了比较,并针对日经股票交易所进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sine Cosine Algorithm for Simple recurrent neural network Tuning for Stock Market Prediction
Deep artificial neural networks have recently gained popularity in the time series forecasting literature. Recurrent neural networks’ higher suitability for this type of problem is the reason why this type of network has been chosen over other deep neural network approaches. Due to the number of parameters used the simplicity of these networks is considerable. This characteristic makes deep recurrent neural networks highly suitable for the problems of forecasting. Unfortunately, finding recurrent neural architecture for each specific task is NP-hard, therefore employment of metaheuristics is appropriate. Accordingly, the research proposed in this paper tackles tuning simple recurrent neural networks by sine cosine algorithm for stock market prediction. The proposed method’s performance was compared with other metaheuristics and validated against the Nikkei stock exchange.
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