The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.
翻译:预测和分析金融数据是一个非线性、取决于时间的问题。随着机器学习和深层次学习的迅速发展,这项任务可以通过一个有目的设计的网络来更有效地完成。本文件旨在通过使用基因反向网络来提高预测准确性和减少预测错误损失的深度学习结构来提高预测准确性和减少预测错误损失。它提出了一个通用模型,其中包括用于重建价格序列的分阶段空间重建(PSR)方法和General Aversarial 网络(GAN),这是一个极难预测其发展趋势的任务。预测和分析金融数据是一个非线性、不取决于时间的问题。随着机器学习和深层次学习的迅速发展,这项任务可以通过一个设计网络来更有效地完成。本文的目的是通过使用基因反向基本指标信息来提高预测深度学习结构的预测准确性和尽量减少预测错误损失。然后CNNC将评估数据是否由LSTM预测,General Aversarial 和General Aversarial 网络(GAN)是结合两个线性网络的,它们作为导向长期短期内存(LTM)模型的快速准确性模型和不断改进的准确性分析结果。LMS的精确性研究显示,这在改进的准确性系统中的准确性系统中的准确性分析结果似乎提供了更好的根基流结果。