Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data
Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data
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DOI：
10.1371/journal.pone.0212320
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发表时间：
2019-02-15
期刊：
影响因子：
3.7
通讯作者：
Kim, Ha Young
中科院分区：
文献类型：
--
作者：
Kim, Taewook;Kim, Ha Young
预测股票价格在制定交易策略或确定买入或卖出股票的适当时机方面起着重要作用。我们提出了一种称为特征融合长短期记忆卷积神经网络（LSTM-CNN）模型的模型，该模型结合了从相同数据的不同表示（即股票时间序列和股票图表图像）中学习到的特征来预测股票价格。该模型由LSTM和CNN组成，用于提取时间特征和图像特征。我们使用SPDR S&P 500 ETF数据测量了所提出模型相对于单一模型（CNN和LSTM）的性能。我们的特征融合LSTM-CNN模型在预测股票价格方面优于单一模型。此外，我们发现蜡烛图是最适合用于预测股票价格的股票图表图像。因此，这项研究表明，预测误差可以有效地减少使用相同的数据，而不是单独使用这些功能的时间和图像特征的组合。
Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. We propose a model, called the feature fusion long short-term memory-convolutional neural network (LSTM-CNN) model, that combines features learned from different representations of the same data, namely, stock time series and stock chart images, to predict stock prices. The proposed model is composed of LSTM and a CNN, which are utilized for extracting temporal features and image features. We measure the performance of the proposed model relative to those of single models (CNN and LSTM) using SPDR S&P 500 ETF data. Our feature fusion LSTM-CNN model outperforms the single models in predicting stock prices. In addition, we discover that a candlestick chart is the most appropriate stock chart image to use to forecast stock prices. Thus, this study shows that prediction error can be efficiently reduced by using a combination of temporal and image features from the same data rather than using these features separately.