Публікація: Machine Learning in Classification Time Series with Fractal Properties
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Дата
2019
Назва журналу
ISSN журналу
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Видавництво
Анотація
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.
Опис
Ключові слова
fractal time series, binomial stochastic cascade, classification of time series, Hurst exponent, random forest, detecting distributed denial-of-service attacks
Бібліографічний опис
Kirichenko L. Machine Learning in Classification Time Series with Fractal Properties / L. Kirichenko, V. Bulakh, T. Radivilova // Data. – 2019. – vol.4(1) 5. – P. 1–13.