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Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

Author

Listed:
  • Xiaofan Wang

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Lingyu Xu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

Suggested Citation

  • Xiaofan Wang & Lingyu Xu, 2020. "Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion," Future Internet, MDPI, vol. 12(2), pages 1-13, February.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:2:p:34-:d:320300
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    References listed on IDEAS

    as
    1. Haiyan Xu & Yanhui Ding & Jing Sun & Kun Zhao & Yuanjian Chen, 2019. "Dynamic Group Recommendation Based on the Attention Mechanism," Future Internet, MDPI, vol. 11(9), pages 1-15, September.
    2. Wenkuan Li & Peiyu Liu & Qiuyue Zhang & Wenfeng Liu, 2019. "An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism," Future Internet, MDPI, vol. 11(4), pages 1-15, April.
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    Cited by:

    1. Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.

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