Перегляд Кафедра прикладної математики (ПМ) за Тема "classification of time series"
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- ДокументClassification of Multifractal Time Series by Decision Tree Methods(КНУ, 2018) Булах, В. А.; Кіріченко, Л. О.; Радівілова, Т. А.The article considers classification task of model fractal time series by the methods of machine learning. To classify the series, it is proposed to use the meta algorithms based on decision trees. To modeling the fractal time series, binomial stochastic cascade processes are used. Classification of time series by the ensembles of decision trees models is carried out. The analysis indicates that the best results are obtained by the methods of bagging and random forest which use regression trees.
- ДокументMachine Learning in Classification Time Series with Fractal Properties(2019) Кіріченко, Л. О.; Булах, В. А.; Радівілова, Т. А.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.
- ДокументTime Series Classification Based on Fractal Properties(2018) Булах, В. А.; Кіріченко, Л. О.; Радівілова, Т. А.The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.