SDAE-LSTM模型在金融时间序列预测中的应用

针对金融时间序列预测的复杂性和长期依赖性,提出了一种基于深度学习的LSTM神经网络预测模型。利用堆叠去噪自编码从金融时间序列的基本行情数据和技术指标中提取特征,将其作为LSTM神经网络的输入对金融时间序列进行预测;通过LSTM神经网络的长期依赖特性来提高金融时间序列的预测精度。利用股价指数数据,与传统的神经网络的预测结果进行比较,结果表明基于深度学习的LSTM神经网络具有比较高的预测精度。

Application of SDAE-LSTM Model on Financial Time Series Forecasting

Due to complexities and long-term dependencies of financial time series forecasting, this paper proposes a forecasting model with Long Short-Term Memory(LSTM)neural network based on deep learning technique. Firstly,stacked denoising autoencoder architectures are applied for feature extraction from the basic market data and the technical indicators of financial time series. Then, LSTM neural network uses the extracted features as inputs for financial time series forecasting. The accuracy of financial time series forecasting is improved by long-term dependencies characteristics of LSTM neural network. Compared with the traditional neural network, the experimental results show that the LSTM neural network has high forecasting accuracy when combined with deep learning technique by using the stock index data.