EB-dynaRE：基于事件监督学习算法的布朗运动实时调节器及股票趋势预测
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm
摘要：
随着时间的推移，股票价格受到潜在宏观经济因素的影响。跳出了传统的关于市场噪音不可预测性的假设，我们通过马尔可夫决策过程（Markov Decision Process）对股票价格随时间的变化进行了建模。马尔可夫决策过程是一种离散的随机控制过程，有助于在部分随机的情况下做出决策。然后，我们对股票时间序列图进行了“感兴趣区域”（RoI）池化，以预测未来的价格。生成对抗网络（GAN），然后使用一对竞争的监督学习算法的基础上，重新生成未来的股票价格预测的实时的基础上。此外，本研究中使用的监督学习算法是本研究的原创，将有更广泛的用途。通过这些算法的集成，我们能够识别每个特定的宏观经济因素在多大程度上影响布朗/随机市场运动的变化。此外，我们的模型将对其他布朗运动的预测产生更广泛的影响。
英文摘要：
Stock prices are influenced over time by underlying macroeconomic factors. Jumping out of the box of conventional assumptions about the unpredictability of the market noise, we modeled the changes of stock prices over time through the Markov Decision Process, a discrete stochastic control process that aids decision making in a situation that is partly random. We then did a "Region of Interest" (RoI) Pooling of the stock time-series graphs in order to predict future prices with existing ones. Generative Adversarial Network (GAN) is then used based on a competing pair of supervised learning algorithms, to regenerate future stock price projections on a real-time basis. The supervised learning algorithm used in this research, moreover, is original to this study and will have wider uses. With the ensemble of these algorithms, we are able to identify, to what extent, each specific macroeconomic factor influences the change of the Brownian/random market movement. In addition, our model will have a wider influence on the predictions of other Brownian movements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
