【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。更多还原

【Abstract】 Analyzing the hidden relationship between historical tax data and using mathematical models to predict future tax revenue is the focus of tax forecast research.A tax prediction model of long short-term memory(LSTM) recurrent neural network combined with wavelet transform is proposed in this paper.Combining wavelet transform on data preprocessing to remove noise from tax data and improve the generalization ability of the model.The LSTM neural network can better learn the correlation between historical tax data by adding hidden neural units and gated units,and further extract valid state innovations between input sequences,and overcome the long-term dependency problem of recurrent neural networks.Experimental results show that the encoder-decoder structure based on the LSTM neural network can enhance the time step of tax prediction.Compared with the single-step sliding window LSTM neural network model and the gray model based on difference differential equations in the long-term tax prediction,the model and the regression-based autoregressive moving average model(ARIMA) significantly improve the prediction accuracy.更多还原