Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis

Autoregressive Process ◽

Fluctuation Analysis ◽

Fat Tails ◽

Exchange Markets ◽

In this study, we analyzed the multifractality and the source of multifractality of the returns of GBP/USD, EUR/USD, USD/JPY and USD/CHF currencies. In the examination of multifractality we performed the Multifractal Detrended Fluctuation Analysis (MF-DFA). Also, we used shuffled and surrogated data that was derived from the Statically Transformed Autoregressive Process (STAP) method to determine the source of multifractality. According to the results, GBP/USD returns have monofractal features, whereas EUR/USD, USD/JPY and USD/CHF returns have multifractal behaviours. The tests concerning the source of multifractality indicated that the reason of multifractality for EUR/USD and USD/JPY returns is fat-tails of the probability density function of returns, whereas the reason of multifractality of USD/CHF returns are both long memory and fat tails. Also we have seen that there is an ambiguous relationship between the liquidity of the currency market and multifractality.

Author response for "Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis"

10.1002/2050-7038.12564/v2/response1 ◽

2020 ◽

Author(s):

Eluri N.V.D.V. Prasad ◽

P.K. Dash

Keyword(s):

Fault Analysis ◽

Author Response ◽

Fluctuation Analysis ◽

Dc Microgrid ◽

Photovoltaic Generation ◽

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2021.126118 ◽

2021 ◽

pp. 126118

Author(s):

Javier Gómez-Gómez ◽

Rafael Carmona-Cabezas ◽

Ana B. Ariza-Villaverde ◽

Eduardo Gutiérrez de Ravé ◽

Francisco José Jiménez-Hornero

Keyword(s):

Fluctuation Analysis ◽

EEG based cognitive task classification using multifractal detrended fluctuation analysis

Cognitive Neurodynamics ◽

10.1007/s11571-021-09684-z ◽

2021 ◽

Author(s):

G. Gaurav ◽

R. S. Anand ◽

Vinod Kumar

Keyword(s):

Cognitive Task ◽

Fluctuation Analysis ◽

Task Classification ◽

Multifractal detrended fluctuation analysis of optogenetic modulation of neural activity

10.1117/12.2007689 ◽

2013 ◽

Cited By ~ 1

Author(s):

S. Kumar ◽

L. Gu ◽

N. Ghosh ◽

S. K. Mohanty

Keyword(s):

Neural Activity ◽

Fluctuation Analysis ◽

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

10.1177/0954406216664547 ◽

2016 ◽

Vol 231 (22) ◽

pp. 4139-4149 ◽

Cited By ~ 1

Author(s):

Du Wenliao ◽

Guo Zhiqiang ◽

Gong Xiaoyun ◽

Xie Guizhong ◽

Wang Liangwen ◽

...

Keyword(s):

Empirical Mode Decomposition ◽

Fluctuation Analysis ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

Kolmogorov Smirnov ◽

Detrended Fluctuation ◽

Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.