GENERALIZED APPROACH TO HURST EXPONENT ESTIMATING BY TIME SERIES
Анотація
This paper presents a generalized approach to the fractal analysis of self-similar random processes by short time series. Several stages of the fractal analysis are proposed. Preliminary time series analysis includes the removal of short-term dependence, the identification of true long-term dependence and hypothesis test on the existence of a self-similarity property. Methods of unbiased interval estimation of the Hurst exponent in cases of stationary and non-stationary time series are discussed. Methods of estimate refinement are proposed. This approach is applicable to the study of self-similar time series of different nature.
Класифікація
Ідентифікатори
Рецензії (0)
Написати рецензіюРецензій ще немає. Будьте першим!
Схожі роботи
Time Series Classification Based on Fractal Properties
Схоже за: Fractal and DNA sequence analysis · Time Series Analysis and Forecasting · Complex Systems and Time Series Analysis
Time Series Classification Based on Fractal Properties
Схоже за: Fractal and DNA sequence analysis · Time Series Analysis and Forecasting · Complex Systems and Time Series Analysis
Two Approaches to Machine Learning Classification of Time Series Based on Recurrence Plots
Схоже за: Fractal and DNA sequence analysis · Time Series Analysis and Forecasting · Complex Systems and Time Series Analysis
Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload
Схоже за: Fractal and DNA sequence analysis · Complex Systems and Time Series Analysis
Dynamics of COVID-19 Development: New Data and New Estimates from Wavelet Analysis
Схоже за: Fractal and DNA sequence analysis · Complex Systems and Time Series Analysis
Binary Classification of Fractal Time Series by Machine Learning Methods
Схоже за: Fractal and DNA sequence analysis · Complex Systems and Time Series Analysis