Unbiased detrended fluctuation analysis: Long-range correlations in very short time series

2018 ◽  
Vol 505 ◽  
pp. 179-189 ◽  
Author(s):  
Qianshun Yuan ◽  
Changgui Gu ◽  
Tongfeng Weng ◽  
Huijie Yang
Keyword(s):  
Time Series ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Short Time Series ◽  
Long Range Correlations ◽  
Short Time ◽  
Detrended Fluctuation

scholarly journals Fractal Analysis of Deep Ocean Current Speed Time Series

2017 ◽  
Vol 34 (4) ◽  
pp. 817-827 ◽  
Author(s):  
Laura Cabrera-Brito ◽  
German Rodriguez ◽  
Luis García-Weil ◽  
Mercedes Pacheco ◽  
Esther Perez ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Single Point ◽  
Deep Ocean ◽  
Current Speed ◽  
Ocean Current ◽  
Fluctuation Analysis ◽  
Long Range Correlations ◽  
Detrended Fluctuation

AbstractFractal properties of deep ocean current speed time series, measured at a single-point mooring on the Madeira Abyssal Plain at 1000- and 3000-m depth, are explored over the range between one week and 5 years, by using the detrended fluctuation analysis and multifractal detrended fluctuation analysis methodologies. The detrended fluctuation analysis reveals the existence of two subranges with different scaling behaviors. Long-range temporal correlations following a power law are found in the time-scale range between approximately 50 days and 5 years, while a Brownian motion–type behavior is observed for shorter time scales. The multifractal analysis approach underlines a multifractal structure whose intensity decreases with depth. The analysis of the shuffled and surrogate versions of the original time series shows that multifractality is mainly due to long-range correlations, although there is a weak nonlinear contribution at 1000-m depth, which is confirmed by the detrended fluctuation analysis of volatility time series.

Measuring efficiency of international crude oil markets: A multifractality approach

2015 ◽  
Vol 36 ◽  
pp. 1560013
Author(s):  
H. M. Niere
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Strong Dependence ◽  
Fluctuation Analysis ◽  
Long Range Correlations ◽  
Oil Markets ◽  
Detrended Fluctuation ◽  
Hurst Exponents

The three major international crude oil markets are treated as complex systems and their multifractal properties are explored. The study covers daily prices of Brent crude, OPEC reference basket and West Texas Intermediate (WTI) crude from January 2, 2003 to January 2, 2014. A multifractal detrended fluctuation analysis (MFDFA) is employed to extract the generalized Hurst exponents in each of the time series. The generalized Hurst exponent is used to measure the degree of multifractality which in turn is used to quantify the efficiency of the three international crude oil markets. To identify whether the source of multifractality is long-range correlations or broad fat-tail distributions, shuffled data and surrogated data corresponding to each of the time series are generated. Shuffled data are obtained by randomizing the order of the price returns data. This will destroy any long-range correlation of the time series. Surrogated data is produced using the Fourier-Detrended Fluctuation Analysis (F-DFA). This is done by randomizing the phases of the price returns data in Fourier space. This will normalize the distribution of the time series. The study found that for the three crude oil markets, there is a strong dependence of the generalized Hurst exponents with respect to the order of fluctuations. This shows that the daily price time series of the markets under study have signs of multifractality. Using the degree of multifractality as a measure of efficiency, the results show that WTI is the most efficient while OPEC is the least efficient market. This implies that OPEC has the highest likelihood to be manipulated among the three markets. This reflects the fact that Brent and WTI is a very competitive market hence, it has a higher level of complexity compared against OPEC, which has a large monopoly power. Comparing with shuffled data and surrogated data, the findings suggest that for all the three crude oil markets, the multifractality is mainly due to long-range correlations.

scholarly journals Effects of Exponential Trends on Correlations of Stock Markets

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ai-Jing Lin ◽  
Peng-Jian Shang ◽  
Hua-Chun Zhou
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Long Range ◽  
Lower Order ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Scaling Analysis ◽  
Analysis Method ◽  
Long Range Correlations ◽  
Detrended Fluctuation

Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in time series. In this paper, DFA is employed to discuss the long-range correlations of stock market. The effects of exponential trends on correlations of Hang Seng Index (HSI) are investigated with emphasis. We find that the long-range correlations and the positions of the crossovers of lower order DFA appear to have no immunity to the additive exponential trends. Further, our analysis suggests that an increase in the DFA order increases the efficiency of eliminating on exponential trends. In addition, the empirical study shows that the correlations and crossovers are associated with DFA order and magnitude of exponential trends.

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

scholarly journals Time-Scaling Properties of Sunshine Duration Based on Detrended Fluctuation Analysis over China

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 83 ◽  
Author(s):  
Lei Jiang ◽  
Jiping Zhang ◽  
Yan Fang
Keyword(s):  
Time Series ◽  
Detrended Fluctuation Analysis ◽  
Sunshine Duration ◽  
Underlying Surface ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Long Range Correlations ◽  
Time Scaling ◽  
Daily Sunshine ◽  
Detrended Fluctuation

The spatial and temporal variabilities of the daily Sunshine Duration (SSD) time series from the Chinese Meteorological Administration during the 1954–2009 period are examined by the Detrended Fluctuation Analysis (DFA) method. As a whole, weak long-range correlations (LRCs) are found in the daily SSD anomaly records over China. LRCs are also verified by shuffling the SSD records. The proportion of the stations with LRCs accounts for about 97% of the total. Many factors affect the scaling properties of the daily SSD records such as sea-land difference and Tibetan Plateau landform and so on. We find land use and land cover as one of the important factors closely links to LRCs of the SSD. Strong LRCs of the SSD mainly happen in underlying surface of deserts and crops, while weak LRCs occur in forest and grassland. Further studies of scaling behaviors are still necessary to be performed due to the complex underlying surface and climate system.

scholarly journals Scaling behaviour of the global tropopause

2009 ◽  
Vol 9 (2) ◽  
pp. 677-683 ◽  
Author(s):  
C. Varotsos ◽  
M. Efstathiou ◽  
C. Tzanis
Keyword(s):  
Long Range ◽  
Detrended Fluctuation Analysis ◽  
Sea Surface ◽  
Fluctuation Analysis ◽  
Tropopause Height ◽  
Time Intervals ◽  
Long Range Correlations ◽  
Scaling Behaviour ◽  
Detrended Fluctuation ◽  
Global Mean

Abstract. Detrended fluctuation analysis is applied to the time series of the global tropopause height derived from the 1980–2004 daily radiosonde data, in order to detect long-range correlations in its time evolution. Global tropopause height fluctuations in small time-intervals are found to be positively correlated to those in larger time intervals in a power-law fashion. The exponent of this dependence is larger in the tropics than in the middle and high latitudes in both hemispheres. Greater persistence is observed in the tropopause of the Northern than in the Southern Hemisphere. A plausible physical explanation of the fact that long-range correlations in tropopause variability decreases with increasing latitude is that the column ozone fluctuations (that are closely related with the tropopause ones) exhibit long range correlations, which are larger in tropics than in the middle and high latitudes at long time scales. This finding for the tropopause height variability should reduce the existing uncertainties in assessing the climatic characteristics. More specifically the reliably modelled values of a climatic variable (i.e. past and future simulations) must exhibit the same scaling behaviour with that possibly existing in the real observations of the variable under consideration. An effort has been made to this end by applying the detrended fluctuation analysis to the global mean monthly land and sea surface temperature anomalies during the period January 1850–August 2008. The result obtained supports the findings presented above, notably: the correlations between the fluctuations in the global mean monthly land and sea surface temperature display scaling behaviour which must characterizes any projection.

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