关键词: 金融时间序列, 深度学习, LSTM神经网络

Abstract: 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.

Key words: financial time series, deep learning, Long Short-Term Memory（LSTM） neural network