Affiliation:
1. Peter the Great St. Petersburg Polytechnic University
Abstract
Analyzing self-similar processes in various fields requires fast and efficient processing of large amounts of data. The frequency and time scalability of self-similar processes require analysis over multiple time periods. Thus it is necessary to develop effective methods of data aggregation. The paper considers the hierarchical organization of time series and multidimensional aggregation based on a graph. The effectiveness of the proposed aggregation methods and their applicability to the analysis of self-similar processes in various fields are evaluated.
Publisher
Belarusian State University
Subject
Mathematical Physics,Statistical and Nonlinear Physics
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1 articles.
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