关键词: 比特币价格, 长短时记忆网络, 小波变换, 自适应噪声完备经验模态分解, 样本熵

Abstract: For the reason that Bitcoin price is highly nonlinear and non-stationary,this paper proposes four mixed forecasting model based on Long Short-Term Memory (LSTM) model to get better prediction performance.Firstly,we use Wavelet Transform (WT) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose and reconstruct the original sequence.Then,we introduce Sample Entropy (SE) to optimize the reconstruction.Finally,we predict the reconstructed sub-sequences respectively using LSTM and superpose the outcomes yields of prediction results.To evaluate the prediction performance,three evaluation functions are used,which are RMSE,MAPE and TIC.Besides,the results are compared with single LSTM model prediction result.The research shows that the prediction accuracy of mixed model is better than that of the single model,and the introduction of Sample Entropy can effectively reduce prediction error.

Key words: Bitcoin price, Long short-term memory network, Wavelet transform, Complete ensemble empirical mode decomposition with adaptive noise, Sample entropy