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Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

机译：改进的LS-SVR和AGO相结合的时间序列自适应在线预测方法

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摘要

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.
机译：复杂系统的故障或健康状况预测近年来引起了更多的关注。复杂的系统通常表现出复杂的动态行为和不确定性，这使得难以建立精确的物理模型。因此，在实际中使用复杂系统的时间序列来实现预测。针对时间序列在线预测，本文提出了一种基于灰色系统理论和增量学习算法的提高预测精度的新方法。在这种方法中，首先采用具有原始时间序列的累积生成操作（AGO）来提高数据质量和规则性；然后通过改进的LS-SVR模型进行预测，从而通过增量学习简化了计算过程。最后，进行逆累积生成操作（IAGO），以获得预测结果。预测实验结果初步表明，该方案具有良好的预测精度和较少的计算时间，是一种有效的预测方法。该方法将在实际应用中有用。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第12期|985930.1-985930.12|共12页
  • 作者

    Guo Yangming; Zhang Lu; Cai Xiaobin; Ran Congbao; Zhai Zhengjun; Ma Jiezhong;

  • 作者单位

    School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;

    School of Software and Microelectronics, Northwestern Polytechnical University, Xi'an 710072, China;

    Science and Technology Commission, Aviation Industry Corporation of China, Beijing 100068, China;

    School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;

    School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;

    School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;

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