The Different Types of Noise and How They Effect Data Analysis |
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Authors: | Prof Dr Armin Bunde |
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Affiliation: | Justus-Liebig-Universität Gießen, Institut für Theoretische Physik, Heinrich-Buff-Ring 16, 35392 Gießen, Germany |
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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. |
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Keywords: | Detrended fluctuation analysis Diffusion Long-range correlations Noise |
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