计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 39-45.doi: 10.11896/jsjkx.210600124
张宁, 方靖雯, 赵雨宣
ZHANG Ning, FANG Jing-wen, ZHAO Yu-xuan
摘要： 聚焦于具有极度非线性、非平稳性等特征的比特币价格预测问题,在长短时记忆网络(Long Short-Term Memory,LSTM)基础上构建了4个混合预测模型,利用小波变换(Wavelet Transform,WT)以及自适应噪声的完备经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)对序列进行分解与重构,并引入了样本熵(Sample Entropy,SE)进行重构优化,使用LSTM对重构以后的子序列分别进行预测,最后将其叠加得到最终的预测结果。在预测结果的评判上,使用均方根误、平均绝对百分误以及希尔不等系数来进行拟合评价,并将结果与单一LSTM模型进行比较。研究发现混合模型的预测准确性均优于单一模型,且样本熵的引入可有效降低预测误差。
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