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.