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, Complete ensemble empirical mode decomposition with adaptive noise, Long short-term memory network, Sample entropy, Wavelet transform