  To assess whether a given time series can be modeled by a stochastic process
possessing long range correlation one usually applies one of two types of
analysis methods: the spectral method and the random walk analysis. The first
objective of this work is to show that each one of these methods used alone can
be susceptible to producing false results. We thus advocate an integrated
approach which requires the use of both methods in a consistent fashion. We
provide the theoretical foundation of this approach and illustrate the main
ideas using examples. The second objective relates to the observation of long
range anticorrelation (Hurst exponent H < 1/2) in real world time series data.
The very peculiar nature of such processes is emphasized in light of the
stringent condition under which such processes can occur. Using examples we
discuss the possible factors that could contribute to the false claim of long
range anticorrelations and demonstrate the particular importance of the
integrated approach in this case.
