Spark平台下的海表面温度并行预测算法

面对海量的海表面温度数据,如何使用大数据处理平台和新的处理技术来实时处理、分析并预测海表面温度数据,是一个亟待解决的问题。本文基于现阶段的时间序列方法和专家意见,首先,将类比合成方法引入到海表面温度预测应用中;其次,基于Spark平台提出了一种改进的快速DTW算法SparkDTW;最后,为了充分利用通过时间序列挖掘得到的信息,将SparkDTW与SVM相结合,提出了SparkDTW+SVM混合模型,为海表面温度预测的应用研究提供了较好的理论基础和技术支持。实验结果表明,SparkDTW算法预测精度优于SVM,提高了海表面温度预测效率,验证了将类比合成方法应用在海表面温度预测的可行性;SparkDTW+SVM在精度方面要优于SparkDTW和SVM,表明SVM模型能充分利用时间序列挖掘的信息,验证了SparkDTW+SVM在海表面温度预测的有效性。

Surface temperature parallel prediction algorithm under Spark platform

Faced with massive sea surface temperature data, how to use Spark platform and new processing technology to analyze, predict and process sea surface temperature data in real time has become a hot topic. Based on the current time series method and expert opinion, this paper first introduced the analog complexing method into the sea surface temperature prediction application.Then, based on the Spark platform, an improved fast DTW algorithm SparkDTW was proposed. Finally, in order to make full use of the information obtained through time series mining, SparkDTW and SVM were combined, and the SparkDTW +SVM hybrid model was proposed, which provides a good theoretical basis and technical support for the application research of global sea surface temperature prediction. The experimental results show that the prediction accuracy of SparkDTW algorithm is better than SVM, which improves the sea surface temperature prediction efficiency and verifies the feasibility of applying analogy complexing method to sea surface temperature prediction; Spark DTW+SVM is superior to SparkDTW and SVM in accuracy, which indicates that SVM model can make full use of time series mining information to verify the effectiveness of SparkDTW+SVM in sea surface temperature prediction.