The Different Types of Noise and How They Effect Data Analysis

Abstract

Random (noisy) processes can be characterized by the way consecutive data are correlated. The data can be uncorrelated (white noise), short‐range correlated (often called red noise), or long‐range correlated (sometimes called pink noise). Here we describe the properties and applications of these different kinds of noise. We discuss, how they influence (i) the diffusion process, (ii) the occurrence of rare extreme events and (iii) the detection of an external trend that is superimposed on the noise; (ii) and (iii) are particularly relevant in the context of detecting anthropogenic global warming by data analysis.