论文标题
GNSS位置时间序列预测及其在异常和异常检测和地震预测中的专门设计的机器学习算法
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction
论文作者
论文摘要
我们提出了一种简单而有效的监督机器学习算法，该算法是为GNSS位置时间序列预测设计的。该算法有四个步骤。首先，从中减去时间序列的平均值。其次，删除了时间序列的趋势。第三，小波用于分离高和低频。第四，使用身份以及正弦和余弦函数的乘积，得出了许多频率并用于查找隐藏层和输出层之间的权重。在本算法中考虑了观察精度的作用。提出了一项大规模研究，该研究提出了全球三千个位置序列的GNSS站。检查了十七种不同的机器学习算法。这些算法的精度水平与theta的严格统计方法进行了检查。结果表明，除了更快的速度外，最精确的机器学习算法是我们提出的方法。提出了两种算法的应用。在第一个应用中，可以证明可以通过提出的算法检测和删除时间序列中的异常值和异常。在一项大规模研究中，将其他十种时间序列序列异常值检测与所提出的算法进行了比较。该研究表明，所提出的算法在检测异常值时的准确性约为3.22％。在第二次应用中，研究了算法对地震预测的适用性。提出了针对Tohoku 2011地震的案例研究。结果表明，这场地震可能在发生前大约2小时被预测，仅基于845 Geonet站时间序列。与四个不同研究的比较表明，地震时间的预测有所改善。
We present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. This algorithm has four steps. First, the mean value of the time series is subtracted from it. Second, the trends in the time series are removed. Third, wavelets are used to separate the high and low frequencies. And fourth, a number of frequencies are derived and used for finding the weights between the hidden and the output layers, using the product of the identity and sine and cosine functions. The role of the observation precision is taken into account in this algorithm. A large-scale study of three thousand position times series of GNSS stations across the globe is presented. Seventeen different machine learning algorithms are examined. The accuracy levels of these algorithms are checked against the rigorous statistical method of Theta. It is shown that the most accurate machine learning algorithm is the method we present, in addition to being faster. Two applications of the algorithm are presented. In the first application, it is shown that the outliers and anomalies in a time series can be detected and removed by the proposed algorithm. In a large scale study, ten other methods of time series outlier detection are compared with the proposed algorithm. The study reveals that the proposed algorithm is approximately 3.22 percent more accurate in detecting outliers. In the second application, the suitability of the algorithm for earthquake prediction is investigated. A case study is presented for the Tohoku 2011 earthquake. It is shown that this earthquake could have been predicted approximately 2 hours before its happening, solely based on each of the 845 GEONET station time series. Comparison with four different studies show the improvement in prediction of the time of the earthquake.
