基于SSA和AR模型的海面变化预测试验

PREDICTION EXPERIMENT OF SEA-LEVEL CHANGE BASED ON THE SSA AND AR MODEL

以吴淞站1955-2001年月平均潮位序列为基础,采用奇异谱分析(SSA)与自回归模型(AR)相结合的方案(SSA+AR),进行了月平均潮位预测试验。基本思路是对SSA分析的结果选择若干有意义的分量进行序列重建,借助于自回归模型进行分量预测,再对它们进行叠加,从而建立预测模型。本文以1955-1996年数据为基础建立模型,1997-2001年数据作为验证,检验结果表明,两种方法的结合使用显示了较好的效果。

Based on the monthly average tidal records of Wusong tidal gauge station from 1955 to 2001, prediction experiment is made use of singular spectrum analysis （SSA） and auto-regressive mode （AR） in this paper. SSA is used to isolate the T-PCs （prinieipal components） corresponding to sea-level change from the remaining variability and noise. Since the T-PCs are the filtered versions of the raw data, their behavior is more regular than that of the original signal ad more predictable accordingly. In practice advantage of each significant component can be made and reconstruct them. Using AR model to predict each reconstruction components, the summation of all the predicted reconstruction components is the prediction results. Analyzing with the data of 1955-1996 to predict the sea-level changes of 1997-2001, comparing prediction results with the original data, it is found that these two series are fairly comparable. It shows that integration use of these two methods provide an efficient way to predict sea-level chenges.