基于LSTM神经网络的金融时间序列预测

本文提出将小波分析与纳入时间序列依赖特征的长短期记忆(LSTM)神经网络相结合,构建金融时间序列数据预测模型,以克服现有模型对金融时间序列数据非平稳、非线性、序列相关等复杂特征以及数据间非线性交互关系无法反映的缺陷。同时,以道琼斯工业指数日收盘价为例,探究LSTM神经网络对实际金融时间序列数据的预测能力,比较其与多层感知机、支持向量机、K近邻、GARCH四种模型的预测效果。实证结果表明LSTM神经网络具有更高的预测精度,能够有效预测金融时间序列数据的长短期动态变化趋势,说明了其对金融时间序列数据预测的适用性与有效性。此外,对金融时间序列数据进行小波分解与重构,可有效提高LSTM预测模型的泛化能力,以及对长短期动态趋势的预测精度。

Prediction of Financial Time Series Based on LSTM Neural Network

The prediction of financial time series has been a very challenging and meaningful work. An effective prediction model should reflect the complex features such as nonlinearity, non-stationary and sequential correlation that exists in financial time series and conclude the dynamic non-linear interaction effects among the financial economic variables. So, the Long-Short Term Memory (LSTM) deep neural network is used to predict financial time series data. In order to improve the generalization ability of the LSTM model, wavelet analysis is adopted to preprocess the financial time series data to eliminate the noise components of high frequency. That is, wavelet analysis and LSTM deep neural network are combined to forecast financial time series data. At the same, taking the daily closing price of Dow Jones Industrial Average as an example, the prediction ability of LSTM neural network for actual financial data is explored. And this result is compared with the prediction results of Multilayer Perceptron, Support Vector Machine, K-nearest Neighbors and GARCH model. Results show that LSTM neural network can balance the prediction effect of the training set, validation set and test set. And LSTM shows a better prediction effect than shallow machine learning models and GARCH model and better generalization ability. Also, the wavelet decomposition and reconstruction of the financial time series data can effectively improve the generalization ability of the LSTM neural network and can better predict the long-term dynamic trend of the financial time series data. It proves the applicability and effectiveness of LSTM neural network in the area of financial time series prediction, and it is of great significance to monitor the risk of securities market and provide investors with investment suggestions.