The investigation of chaos in time series is the basis of prediction with chaos theory. Though the science of chaos is
a burgeoning field and the available methods to investigate the existence of chaos in a time series are in the state of
infancy, a wide variety of methods are available. Among these methods, the correlation dimension method is the most
popular one. According to this method, a finite correlation dimension is a sign of deterministic chaos, which is
understood as the principal. However, a finite correlation dimension may also be observed from a linear stochastic
process. Therefore, it is necessary to confirm the absence of linearity in the data to verify the results with application of
the correlation dimension method. In this paper, after the reconstruction of phase space, the correlation dimension
method was employed to analyze the chaotic characteristics for groundwater depth time series in Hetao Irrigation District
of Yellow River in China. Considering that a finite correlation dimension is only the necessary condition of chaotic
behavior, the surrogate data method which can distinguish nonlinear characteristics of time series was employed to
analyze the chaotic characteristics for the groundwater depth series. As a comparison, classic Lorenz chaos time series
and stochastic white noises were analyzed using the surrogate data method at the same time. The results show that there
is somewhat chaos in the groundwater depth time series in Hetao District in Yellow River Basin. Meanwhile, the
surrogate data method is the necessary complementarity of the correlation method to investigate the chaotic behavior for
time series.
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