  This review considers methods of nonlinear dynamics to apply for analysis of
time series corresponding to information streams on the Internet. In the main,
these methods are based on correlation, fractal, multifractal, wavelet, and
Fourier analysis. The article is dedicated to a detailed description of these
approaches and interconnections among them. The methods and corresponding
algorithms presented can be used for detecting key points in the dynamic of
information processes; identifying periodicity, anomaly, self-similarity, and
correlations; forecasting various information processes. The methods discussed
can form the basis for detecting information attacks, campaigns, operations,
and wars.
