  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.
