Machine Learning in Classification Time Series with Fractal Properties

Кіріченко, Л. О.; Булах, В. А.; Радівілова, Т. А.

Публікація:
Machine Learning in Classification Time Series with Fractal Properties

dc.contributor.author	Кіріченко, Л. О.
dc.contributor.author	Булах, В. А.
dc.contributor.author	Радівілова, Т. А.
dc.date.accessioned	2019-06-19T08:43:01Z
dc.date.available	2019-06-19T08:43:01Z
dc.date.issued	2019
dc.description.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.	uk_UA
dc.identifier.citation	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.	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/9453
dc.language.iso	en	uk_UA
dc.subject	fractal time series	uk_UA
dc.subject	binomial stochastic cascade	uk_UA
dc.subject	classification of time series	uk_UA
dc.subject	Hurst exponent	uk_UA
dc.subject	random forest	uk_UA
dc.subject	detecting distributed denial-of-service attacks	uk_UA
dc.title	Machine Learning in Classification Time Series with Fractal Properties	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication