GNSS time series prediction based on support vector machine

In order to predict the global navigation satellite system (GNSS) time series, under the theoretical framework of deep learning, the traditional empirical risk minimization prediction model has low error, low generalization performance and large dependence on historical data. A time series prediction model is proposed based on support vector machine (SVM) with the principle of structural risk minimization. compared with the multi-layer BP neural network prediction model prediction, the results prove that the SVM prediction model has better time series prediction effect.