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.
翻译:为了进一步克服现有模型在处理高频财务时间序列数据的非固定和非线性特点方面的困难,特别是其薄弱的概括性能力,本文件建议采用基于数据分解方法的混合方法,包括波子变换和单谱分析,以及长期短期内存神经网络(LSTM),以建立一个数据预测模型,财务时间序列由WT和SSA拆解和重建至delois。在扭曲条件下,对具有有效信息的平稳序列进行重建。平稳序列被引入LSTM和预测值获得。由于Dow Jones工业平均指数(DJIA)作为研究对象,DJIA每5分钟的关闭价格分别分为短期(1小时)、中期(3小时)和长期(6小时)。基于基本平均错误(RMSE)、绝对错误(MAE)、绝对错误(MAMEE)和绝对百分比差(MAMEE),以及绝对差差(SMAPE),长期稳定率(MSPE)可以有效地改进LJA的中期预测,长期数据可以显示LJA的准确性。根据原始平均差差率(RIS)的模型,可以改进LMLS的准确性模型和绝对差(M)和绝对差(MAP),长期数据可以使LPA)和远于LPA的精确性总和远期的精确性预测。