Machine Learning in Classification Time Series with Fractal Properties

Lyudmyla Kirichenko, Tamara Radivilova and Vitalii Bulakh
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Lyudmyla Kirichenko: Department of Applied mathematics, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine
Tamara Radivilova: Department of Infocommunication Engineering, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine
Vitalii Bulakh: Department of Applied mathematics, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine

Data, 2018, vol. 4, issue 1, 1-13

Abstract: The article presents a novel method of fractal time series classification by meta-algorithms based on decision trees. The classification objects are fractal time series. For modeling, binomial stochastic cascade processes are chosen. Each class that was singled out unites model time series with the same fractal properties. Numerical experiments demonstrate that the best results are obtained by the random forest method with regression trees. A comparative analysis of the classification approaches, based on the random forest method, and traditional estimation of self-similarity degree are performed. The results show the advantage of machine learning methods over traditional time series evaluation. The results were used for detecting denial-of-service (DDoS) attacks and demonstrated a high probability of detection.

Keywords: fractal time series; binomial stochastic cascade; classification of time series; Hurst exponent; random forest; detecting distributed denial-of-service attacks (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
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