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Foreign Currency Exchange Rates Prediction using CGP and Recurrent Neural Network

机译：外汇汇率使用CGP和经常性神经网络预测

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摘要

Feedback in Neuro-Evolution is explored and evaluated for its application in devising prediction models for foreign currency exchange rates. A novel approach to foreign currency exchange rates forecasting based on Recurrent Neuro-Evolution is introduced. Cartesian Genetic Programming (CGP) is the algorithm deployed for the forecasting model. Recurrent Cartesian Genetic Programming evolved Artificial Neural Network (RCGPANN) is demonstrated to produce computationally efficient and accurate model for forex prediction with an accuracy of as high as 98.872 % for a period of 1000 days. The approach utilizes the trends that are being followed in historical data to predict five currency rates against Australian dollar. The model is evaluated using statistical metrics and compared. The computational method outperforms the other methods particularly due to its capability to select the best possible feature in real time and the flexibility that the system provides in feature selection, connectivity pattern and network.
机译：探索了神经演化的反馈，并在设计外币汇率预测模型方面的应用中进行了反馈。介绍了一种基于反复性神经演变的外币汇率预测的新方法。笛卡尔遗传编程（CGP）是为预测模型部署的算法。经常性的笛卡尔遗传编程演进式人工神经网络（RCGPann）被证明是为了生产用于外汇预测的计算有效和准确的模型，精度高达98.872％，为1000天。该方法利用历史数据中正在遵循的趋势预测澳大利亚美元的五个货币税率。使用统计指标进行评估模型并进行比较。计算方法占据了其他方法，特别是由于其能力实时选择最佳特征以及系统在特征选择，连接模式和网络中提供的灵活性。

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