基于XGBoost和LSTM模型的金融时间序列预测

随着人工智能快速发展,深度学习模型预测金融时间序列成为热点问题.数据及特征选取是决定模型效果的重要环节,用XGBoost模型进行特征优化并预测黄金价格涨跌趋势,再与LSTM模型比较预测效果.用XGBoost分析动量因子特征重要性并选取有效指标;形态因子做历史回测并选取胜率较高的K线指标,预测准确率提升1.5％.以相同因子为LSTM模型特征值预测准确率提升6.5％,达到80％.以欧元和浦发银行股价数据为样本均证实K线指标有效且LSTM模型预测效果优于XGBoost.

Financial Time Series Forecasting Based on XGBoost and LSTM Models

With the rapid development of artificial intelligence, deep learning model prediction of financial time series has become a hot issue. The selection of data and features is important for the effect of the model. XGBoost is used to optimize the features and predict the trend of gold price trend, and then the prediction effect is compared with LSTM. XGBoost is used to analyze the importance of momentum factors and select effective features. The morphological factors are tested on historical data,and the candlestick chart with higher accuracy rate are selected, the prediction accuracy is increased by 1.5 percent.The same factors are used in LSTM, and the prediction accuracy is increased by 6.5 percent to 80 percent. Taking Euro and SPD Bank stock price data as samples, it is proved that the prediction effect of LSTM is better than that of XGBoost.