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Publication
Self-similarity principle: the reduced description of randomness
| Name: | Description: | Size: | Format: | |
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| 1.86 MB | Adobe PDF |
Advisor(s)
Abstract(s)
A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented.
The proposed analytical function helps to fit the response of many complex systems when their
recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of
economical, meteorological and other complex data when the mathematical model is absent but the reduced
description in terms of some universal set of the fitting parameters is necessary. This fitting function
is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature
surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of
initial data set. The general conditions of application of this fitting method describing the response of many
complex systems and the forecast possibilities are discussed.
Description
Keywords
Self-similar (fractal) processes Solutions of the functional equations Complex systems Fit of economical Weather data series