Journal of Marine Sciences ›› 2019, Vol. 37 ›› Issue (1): 9-14.DOI: 10.3969/j.issn.1001-909X.2019.01.002

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Study on time series prediction model of sea surface temperature based on Ensemble Empirical Mode Decomposition and Autoregressive Integrated Moving Average

ZHANG Ying1, TAN Yan-chun*2, PENG Fa-ding1, LIAO Xing-jie1, YU Yu-xin2   

  1. 1. School of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088, China;
    2. College of Electronical and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
  • Received:2018-08-17 Revised:2018-12-20 Online:2019-03-15 Published:2022-11-21

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Abstract: Various types of ocean detection data with high spatial and temporal resolution provides possibilities for the application of signal decomposition and machine learning algorithms. In order to establish an effective sea surface temperature (SST) prediction model, the high time resolution of the SST and the fusion of products were used in this study, introducing the ensemble empirical mode decomposition (EEMD) in the field of signal processing and autoregressive integrated moving average model (ARIMA) in the field of machine learning. Firstly, the SST data were decomposed into several frequency sequences by EEMD method, which was the most suitable for decomposing natural signals. After that the ARIMA was used to predict the sequence of frequencies, and then the predicted results of each sequence were combined. Compared with the previous method of directly using SST data to build prediction model, this method achieves higher accuracy because of its abundant data, and provides a new way for better SST prediction.

Key words: Ensemble Empirical Mode Decomposition, machine learning, Autoregressive Integrated Moving Average Model, sea surface temperatures

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