论文标题
基于生成的对抗性和卷积神经网络的深度学习，用于财务时间序列预测
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions
论文作者
论文摘要
在大数据时代，研究人员在各个领域采用了深度学习和智能数据挖掘技术解决方案。对股票市场数据的预测和分析在当今经济中代表了至关重要的作用，并且由于市场的趋势非常复杂，混乱，并且在高度动态的环境中发展，因此对专家的挑战是一项重大挑战。有许多意图应对这一挑战的领域有许多研究，机器学习方法一直是其中许多人的重点。机器学习算法有多种模型能够获得有能力的预先观察结果。本文建议实施生成的对抗网络（GAN），该网络由双向长期记忆（LSTM）和卷积神经网络（CNN）组成，称为BI-LSTM-CNN，以生成与现有的现有财务数据一致的合成数据，从而可以保留具有正面或负趋势的股票的现有财务数据，以预测未来趋势的正面趋势。这种与以前解决方案不同的提议解决方案的新颖性是，本文介绍了混合系统（BISTM-CNN）的概念，而不是唯一的LSTM模型。它是从多个股票市场（例如TSX，SHCOMP，KOSPI 200和S＆P 500）收集的数据，该数据提出了一种适应性的混合系统，以预测股票市场价格的趋势，并对几种常用的几种经常使用的机器学习原型进行了全面评估，并且可以得出的结论是，该拟议的解决方案的方法超过了概念的模型。此外，在上述工作的研究阶段，在投资者和致力于技术领域的研究人员之间发现了差距。
In the big data era, deep learning and intelligent data mining technique solutions have been applied by researchers in various areas. Forecast and analysis of stock market data have represented an essential role in today's economy, and a significant challenge to the specialist since the market's tendencies are immensely complex, chaotic and are developed within a highly dynamic environment. There are numerous researches from multiple areas intending to take on that challenge, and Machine Learning approaches have been the focus of many of them. There are multiple models of Machine Learning algorithms been able to obtain competent outcomes doing that class of foresight. This paper proposes the implementation of a generative adversarial network (GAN), which is composed by a bi-directional Long short-term memory (LSTM) and convolutional neural network(CNN) referred as Bi-LSTM-CNN to generate synthetic data that agree with existing real financial data so the features of stocks with positive or negative trends can be retained to predict future trends of a stock. The novelty of this proposed solution that distinct from previous solutions is that this paper introduced the concept of a hybrid system (Bi-LSTM-CNN) rather than a sole LSTM model. It was collected data from multiple stock markets such as TSX, SHCOMP, KOSPI 200 and the S&P 500, proposing an adaptative-hybrid system for trends prediction on stock market prices, and carried a comprehensive evaluation on several commonly utilized machine learning prototypes, and it is concluded that the proposed solution approach outperforms preceding models. Additionally, during the research stage from preceding works, gaps were found between investors and researchers who dedicated to the technical domain.
