计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 437-443.doi: 10.11896/jsjkx.200300091
文豪, 陈昊
WEN Hao, CHEN Hao
摘要: 分析历史税收数据之间的隐藏关系,利用数学模型来预测未来的税收收入是税收预测的研究重点。在此,提出了一种结合小波变换的长短期记忆(LSTM)循环神经网络的税收预测模型。在数据预处理上结合小波变换来去除税收数据中的噪声,提高模型的泛化能力。LSTM神经网络通过加入隐藏神经单元和门控单元能够更好地学习到历史税收数据之间的相关关系,并进一步提取有效的输入序列间的状态新息,而且解决了循环神经网络的长期依赖问题。实验结果表明,基于LSTM神经网络的编码器-解码器结构能够增强税收预测的时间步长,在中长期的税收预测中相比单步滑动窗口的LSTM神经网络模型以及基于差分微分方程的灰色模型和基于回归的自回归移动平均模型(ARIMA),在预测精度上有明显提升。
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