Machine Learning Classification of Multifractional Brownian Motion Realizations - Kirichenko, L.; Radivilova, T.; Bulakh, V. (ХНУРЕ, 2020)
A comparative analysis of machine learning classification of stochastic time series based on their multifractal properties is proposed. Multifractal time series were obtained by generating realizations of fractional Brownian motion in multifractal time. The features for classification were statistical, fractal and recurrent characteristics calculated for each time series. The various machine learning classifiers were chosen for classification: bagging with classification and regression decision trees, random forest with classification and regression decision trees, fully connected perceptron and recurrent neural network. Both cumulative time series of multifractal Brownian motion and time series increments were carried out. It was shown that in general, classification accuracy is higher when using series of increments. When classifying realizations of multifractional Brownian motion, bagging and recurrent neural network showed the best accuracy.
Keywords: multifractal; multifractional Brownian motion; classification of time series; features; random forest; bagging; recurrent neural network