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Butterfly-effects in similarity analyses of time series
Leading article: 2020. February (MIAU No. 258.)
(Previous article: MIAU No. 257.)
Keywords: consistence, ranking, relativity, symmetry of functions
Abstract: The objective of the paper is to demonstrate a butterfly-effect based on a real but anonymous data asset where each step can be reproduced. The problem is: how can we classify personal time series in frame of similarity analyses? The butterfly-effect is the unlimited difference between the result of the classification based on a given data asset and the result of an other classification based on a data asset having just one single record more as input – in this case having data about ¼ sec where the used length of the time series can be over 100 or 1000. The differences will be derived through the ranked inputs – especially in case of data having the same value. The similarity analysis is a typical ranking-oriented modelling schema where these special effects can be detected at once – without any further manipulations. The similarity analyses produce model chains and so, the symmetry-driven similarity analyses can have butterfly-effects in a consistence-oriented model structure too.
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