论文标题
使用具有可训练的初始隐藏状态的LSTM建模财务时间序列
Modeling Financial Time Series using LSTM with Trainable Initial Hidden States
论文作者
论文摘要
提取以前未知的模式和时间序列中的信息是许多现实世界应用的核心。在这项研究中，我们介绍了一种使用深度学习模型来建模财务时间序列的新方法。我们使用配备了可训练的初始隐藏状态的长期短期内存（LSTM）网络。通过学习重建时间序列，提出的模型可以代表具有参数的高维度序列数据。对韩国股票市场数据的实验表明，该模型能够捕获其潜在空间中大量股票价格之间的相对相似性。此外，该模型还能够预测潜在空间的未来股票趋势。提出的方法可以帮助识别许多时间序列之间的关系，并且可以应用于财务应用，例如优化投资组合。
Extracting previously unknown patterns and information in time series is central to many real-world applications. In this study, we introduce a novel approach to modeling financial time series using a deep learning model. We use a Long Short-Term Memory (LSTM) network equipped with the trainable initial hidden states. By learning to reconstruct time series, the proposed model can represent high-dimensional time series data with its parameters. An experiment with the Korean stock market data showed that the model was able to capture the relative similarity between a large number of stock prices in its latent space. Besides, the model was also able to predict the future stock trends from the latent space. The proposed method can help to identify relationships among many time series, and it could be applied to financial applications, such as optimizing the investment portfolios.
