The Different Types of Noise and How They Effect Data Analysis

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.

Automated summary

Random processes can be categorized based on the correlation of consecutive data, which can be uncorrelated (white noise), short-range correlated (red noise), or long-range correlated (pink noise). This article explores the properties and applications of these different types of noise and discusses how they impact diffusion processes, occurrence of rare extreme events, and detection of external trends amidst the noise – the latter two being particularly relevant in the context of analyzing data to identify anthropogenic global warming.