Radivilova, T.Kirichenko, L.Bulakh, V.2023-04-132023-04-132019Radivilova T. Comparative analysis of machine learning classification оf time series with fractal properties / T. Radivilova, L. Kirichenko, V. Bulakh // 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL*2019) : сonference proceedings, Sozopol, Bulgaria, 6-8 September 2019. – P. 557–560.https://openarchive.nure.ua/handle/document/22624The article analyses the classification of time series according to their fractal properties by machine learning. The classification was carried out using neural networks and the random forest method. Objects were the model fractal time series with given the Hurst exponent. Each class was a set of time series with the Hurst exponent values in a predetermined range. Input features were the values of time series. It was demonstrated that in this case the classification accuracy is high enough. The most accurate classification results were obtained using recurrent neural network. The proposed method can be readily used in practice for recognition, classification and clustering of time series with fractal properties.fractal time seriestime series classificationHurst exponentrandom forestneural networks