Previsão dos preços de abertura，mínima e máxima de índices de financeiros usando a associação de redes neurais LSTM
Previsão dos preços de abertura, mínima e máxima de índices de mercados financeiros usando a associação de redes neurais LSTM
摘要：
为了做出好的投资决策，投资者知道如何对金融时间序列进行好的分析是至关重要的。在这种情况下，对股票价格的价值和趋势的预测研究变得更加相关。目前，有不同的方法来处理这项任务。两个主要的是股票价格和技术指标的历史分析，以及对市场的新闻，博客和推文中的情绪分析。一些最常用的统计和人工智能技术是遗传算法，支持向量机（SVM）和人工神经网络的不同架构。这项工作提出了一种基于三个不同LSTM神经网络关联的模型的改进，每个神经网络并行预测分析后第二天证券交易所指数的开盘价、最低价和最高价。该数据集由全球最大的证券交易所的10多个指数的历史数据组成。结果表明，该模型能够预测趋势和股票价格与合理的准确性。
英文摘要：
In order to make good investment decisions, it is vitally important for an investor to know how to make good analysis of financial time series. Within this context, studies on the forecast of the values and trends of stock prices have become more relevant. Currently, there are different approaches to dealing with the task. The two main ones are the historical analysis of stock prices and technical indicators and the analysis of sentiments in news, blogs and tweets about the market. Some of the most used statistical and artificial intelligence techniques are genetic algorithms, Support Vector Machines (SVM) and different architectures of artificial neural networks. This work proposes the improvement of a model based on the association of three distinct LSTM neural networks, each acting in parallel to predict the opening, minimum and maximum prices of stock exchange indices on the day following the analysis. The dataset is composed of historical data from more than 10 indices from the world's largest stock exchanges. The results demonstrate that the model is able to predict trends and stock prices with reasonable accuracy.
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。
