论文标题
Stock2VEC：用代表学习和时间卷积网络的股票市场预测的混合深度学习框架
Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network
论文作者
论文摘要
我们建议开发一个全球混合深度学习框架，以预测股票市场的每日价格。通过表示学习，我们得出了一个名为Stock2Vec的嵌入，该嵌入使我们对不同股票之间关系的见解，而时间卷积层则用于自动捕获串联和整个系列内部和整个系列中的有效时间模式。在标准普尔500指数上进行评估，我们的混合动力框架综合了优势，并且在股票价格预测任务上取得了更好的性能。
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks, while the temporal convolutional layers are used for automatically capturing effective temporal patterns both within and across series. Evaluated on S&P 500, our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
