海表温度(sea surface temperature,SST)是海洋科学研究的重要内容之一.SST的异常波动导致海洋灾害、气象灾害现象时有发生,SST的精确预测对海洋环境保护和海洋经济发展有重要意义.针对SST序列的季节性、非平稳性,首先利用周期趋势分解算法(seasonal-trend decomposition procedure based on loess,STL)对数据进行预处理,分解得到季节分量、趋势分量和残差分量子序列,依次选择相应的预测方法构建组合模型.季节分量应用具有时间嵌入编码模块的Transformer网络预测,充分挖掘序列全局信息,解决时间序列长时间依赖问题;趋势分量应用线性回归模型预测;残差分量应用自回归模型预测.选取南海海域单点SST数据,应用基于STL的SST组合预测模型建模,预测 5 d的SST值.实验结果表明,本文模型在单点SST预测任务中,能够有效捕获SST变化规律,提高预测精度.
Combining forecasting model for sea surface temperature in the South China Sea based on STL
Sea surface temperature(SST)is one of the important contents of marine scientific research.Abnormal fluctuations of SST lead to marine and meteorological disasters.Accurate prediction is of great significance for the study of the marine environmental protection and the development of marine economy.For the seasonality and non-stationarity of the SST sequence,firstly,the data are preprocessed through Seasonal and Trend decomposition using loess(STL),and the decomposition obtains the seasonal component,trend component and residual component.Next,select the corresponding prediction method to build a combining model.Seasonal components are predicted using the Transformer with time embedding encoding module,which fully exploits the global information of the sequence,and solve the problem of capturing long-range dependency;trend components are predicted using linear regression;residual components are predicted using auto regression model.Select the single-point SST data of the South China Sea,and apply the combination prediction model based on STL for modeling to predict the SST value of 5 days.The experimental results show that the model of this paper can effectively capture the variation pattern of SST and improve the prediction accuracy in a single-point SST prediction task.
sea surface temperatureSTLtransformerlinear regressionAR model
NETL
NSTL
万方数据
维普
