Prediction of financial time series using LSTM and data denoising methods
结合 LSTM 与数据降噪方法的金融时间序列预测AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要使用五分钟金融价格数据开展多时距预测，直接对应高频计量与预测方向。
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结合 LSTM 与数据降噪方法的金融时间序列预测
- 研究问题
- 如何结合数据降噪与 LSTM，应对高频金融价格序列的非平稳和非线性特征，并提高不同预测时距下的精度与稳定性？
- 方法与思路
- 作者分别用小波变换和奇异谱分析分解、重构并降噪序列，再输入 LSTM，以道指五分钟收盘价评估一、三、六小时预测。
- 材料陈述的贡献
- 摘要称降噪提高了预测精度、稳定性及泛化表现，且小波与 LSTM 的混合模型优于基础 LSTM 和奇异谱分析混合模型。
- 阅读关注点
- 应核对降噪是否使用预测时点之后的信息、训练测试的时间划分，以及多时距误差比较、调参过程和样本外泛化证据。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未给出误差数值或数据划分，价格预测结果不代表收益预测效果。
原文摘要
In order to further overcome the difficulties of the existing models in dealing with the non-stationary and nonlinear characteristics of high-frequency financial time series data, especially its weak generalization ability, this paper proposes an ensemble method based on data denoising methods, including the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term short-term memory neural network (LSTM) to build a data prediction model, The financial time series is decomposed and reconstructed by WT and SSA to denoise. Under the condition of denoising, the smooth sequence with effective information is reconstructed. The smoothing sequence is introduced into LSTM and the predicted value is obtained. With the Dow Jones industrial average index (DJIA) as the research object, the closing price of the DJIA every five minutes is divided into short-term (1 hour), medium-term (3 hours) and long-term (6 hours) respectively. . Based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and absolute percentage error standard deviation (SDAPE), the experimental results show that in the short-term, medium-term and long-term, data denoising can greatly improve the accuracy and stability of the prediction, and can effectively improve the generalization ability of LSTM prediction model. As WT and SSA can extract useful information from the original sequence and avoid overfitting, the hybrid model can better grasp the sequence pattern of the closing price of the DJIA. And the WT-LSTM model is better than the benchmark LSTM model and SSA-LSTM model.