Elements of nonlinear analysis of information streams

TL;DR

This paper reviews nonlinear dynamics methods like correlation, fractal, wavelet, and Fourier analysis for analyzing Internet information streams, focusing on their applications in detecting key points, anomalies, and forecasting in information processes.

Contribution

It provides a detailed description of various nonlinear analysis methods and their interconnections, highlighting their potential for detecting anomalies and forecasting in Internet information streams.

Findings

Methods can detect key points and anomalies in information streams

Techniques enable forecasting of information process dynamics

Approaches can identify periodicity, self-similarity, and correlations

Abstract

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.