Sea surface temperature prediction algorithm based on STL model

Sea surface temperature is one of the important parameters in marine scientific research, and effective prediction of sea surface temperature is of great significance for marine disaster warning, marine economy and marine ecological environment. Aiming at the characteristics of periodicity, persistence, non-stationarity and non-linearity of sea surface temperature, firstly, the original sea surface temperature series is decomposed into periodic items, trend items and residual items by using the Seasonal-Trend decomposition procedure based on Loess to mine the potential information of sea surface temperature and remove the random noise in the sequence. Combining the advantages of the long short-term memory network model, a neural network is model to predict the sea surface temperature in the next five days. Comparing with the prediction effects of other methods, the experimental results show that the proposed method has better prediction accuracy when predicting sea surface temperature and can effectively predict sea surface temperature.