Abstract
In order to further overcome the difficulties of the existing models in dealing with the nonstationary and nonlinear characteristics of high-frequency financial time series data, especially their 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 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.
Highlights
As the worldwide largest economy, the US has advanced statistical constitutions and a mature financial supervision system, whose financial data are comprehensive, accurate, and credible
For the short-term prediction effect, according to the results shown in Table 3, in terms of prediction accuracy, recurrent neural network (RNN) with a dropout layer is superior to the long-term short-term memory neural network (LSTM) model, and root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 11.63%, 1.96%, and 1.89%, respectively
For the medium-term prediction effect, according to the results shown in Table 4 in terms of prediction accuracy, RNN with a dropout layer is superior to the LSTM model, and RMSE, MAE, and MAPE are decreased by 10.64%, 4.54%, and 4.53%, respectively
Summary
As the worldwide largest economy, the US has advanced statistical constitutions and a mature financial supervision system, whose financial data are comprehensive, accurate, and credible. The US stock market cooperates with other markets in an efficient way and plays an important role in the US financial system, and all these characteristics make the market a good model. Most economic theories and assumptions are based on the study of a developed financial system with a larger and more active stock market, a more mature economy, and a more effective financial supervision system. E importance of the Dow Jones Index has been further recognized in global markets beyond its role in the domestic market. Erefore, forecasting the Dow Jones index is of great significance to the entire financial system. Due to the limitations of the parametric model and features of DJIA price, the parametric model is unsuitable for the Dow Jones index price forecast
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