基于小波降噪和神经网络的GPS高程时序预测模型

全球定位系统（GPS）时序数据预测的工作中发现，通常时序数据中含有的噪声会干扰数据预测的结果.为了降低时序数据中噪声对预测结果的负面影响，将提升小波阈值降噪技术和长短期记忆（LSTM）神经网络相结合，实现一种GPS时序数据降噪预测模型.该模型在预测之前首先利用提升小波与平滑阈值函数对GPS时序数据中的噪声进行剥离，然后构建多层LSTM神经网络对时序数据进行单步预测.通过实验与多种时间序列预测模型进行对比，结果表明所提出的LSTM预测模型对GPS时间序列的预测具有较强的适用性和较高的准确性. 

GPS elevation time series prediction model based on wavelet? denoising technique and neural network

In the work of GPS time series data prediction, it is found that the noise contained in the time series data usually interferes with the results of data prediction. In order to minimize the negative impact of noise on prediction results in time series data as much as possible, a GPS time series data denoising prediction model is realized by combining lifting wavelet threshold denoising technology with long Short-Term Memory (LSTM). The model first uses lifting wavelet and smoothing threshold function to remove noise from GPS time series data before prediction, and then constructs a multi-layer LSTM neural network to predict time series data in one step. Compared with many time series prediction models, the results show that the proposed LSTM prediction model has strong applicability and higher accuracy for GPS time series prediction.