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2021 | OriginalPaper | Chapter

Predicted Distribution Density Estimation for Streaming Data

Authors : Piotr Kulczycki, Tomasz Rybotycki

Published in: Computational Science – ICCS 2021

Publisher: Springer International Publishing

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Abstract

Recent growth in interest concerning streaming data has been forced by the expansion of systems successively providing current measurements and information, which enables their ongoing, consecutive analysis. The subject of this research is the determination of a density function characterizing potentially changeable distribution of streaming data. Stationary and nonstationary conditions, as well as both appearing alternately, are allowed. Within the distribution-free procedure investigated here, when the data stream becomes nonstationary, the procedure begins to be supported by a forecasting apparatus. Atypical elements are also detected, after which the meaning of those connected with new tendencies strengthens, while diminishing elements weaken. The final result is an effective procedure, ready for use without studies and laborious research.

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Metadata
Title
Predicted Distribution Density Estimation for Streaming Data
Authors
Piotr Kulczycki
Tomasz Rybotycki
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-77980-1_43

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