Detrending fluctuation analysis based on moving average filtering

2005 ◽  
Vol 354 ◽  
pp. 199-219 ◽  
Author(s):  
Jose Alvarez-Ramirez ◽  
Eduardo Rodriguez ◽  
Juan Carlos Echeverría
Keyword(s):  
Moving Average ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

scholarly journals Correlation Structures of PM2.5 Concentration Series in the Korean Peninsula

2019 ◽  
Vol 9 (24) ◽  
pp. 5441
Author(s):  
Gyuchang Lim ◽  
Seungsik Min
Keyword(s):  
Cross Correlation ◽  
Moving Average ◽  
Air Pollutant ◽  
Fluctuation Analysis ◽  
Auto Correlation ◽  
Cross Correlation Analysis ◽  
Correlation Structures ◽  
Spatio Temporal ◽  
Pm2.5 Concentration ◽  
Mass Concentrations

In this paper, the authors investigate the idiosyncratic features of auto- and cross-correlation structures of PM2.5 (particulate matter of diameter less than 2.5 μ m ) mass concentrations using DFA (detrended fluctuation analysis) methodologies. Since air pollutant mass concentrations are greatly affected by geographical, topographical, and meteorological conditions, their correlation structures can have non-universal properties. To this end, the authors firstly examine the spatio-temporal statistics of PM2.5 daily average concentrations collected from 18 monitoring stations in Korea, and then select five sites from those stations with overall lower and higher concentration levels in order to make up two groups, namely, G1 and G2, respectively. Firstly, to compare characteristic behaviors of the auto-correlation structures of the two groups, we performed DFA and MFDFA (multifractal DFA) analyses on both and then confirmed that the G2 group shows a clear crossover behavior in DFA and MFDFA analyses, while G1 shows no crossover. This finding implies that there are possibly two different scale-dependent underlying dynamics in G2. Furthermore, in order to confirm that different underlying dynamics govern G1 and G2, the authors conducted DCCA (detrended cross-correlation analysis) analysis on the same and different groups. As a result, in the same group, coupling behavior became more prominent between two series as the scale increased, while, in the different group, decoupling behavior was observed. This result also implies that different dynamics govern G1 and G2. Lastly, we presented a stochastic model, namely, ARFIMA (auto-regressive fractionally integrated moving average) with periodic trends, to reproduce behaviors of correlation structures from real PM2.5 concentration time series. Although those models succeeded in reproducing crossover behaviors in the auto-correlation structure, they yielded no valid results in decoupling behavior among heterogeneous groups.

Scaling properties of image textures: A detrending fluctuation analysis approach

2006 ◽  
Vol 361 (2) ◽  
pp. 677-698 ◽  
Author(s):  
Jose Alvarez-Ramirez ◽  
Eduardo Rodriguez ◽  
Ilse Cervantes ◽  
Juan Carlos Echeverria
Keyword(s):  
Analysis Approach ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Detrending Fluctuation Analysis

Detrending fluctuation analysis of gastric pH reveals long-term correlations accentuated by acid suppression

2003 ◽  
Vol 124 (4) ◽  
pp. A444-A445
Author(s):  
Shingo Yuki ◽  
David Amstrong ◽  
J.S. Capogna ◽  
Francesco Viani ◽  
Andre L. Blum ◽  
...  
Keyword(s):  
Gastric Ph ◽  
Acid Suppression ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis

Abstract P422: Two-kidney One Clip (2k1c) Hypertension Reduces Complexity of Heart Rate Variability in Mice

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Fernanda L Rodrigues ◽  
Luiz E Silva ◽  
Carlos A Silva ◽  
Fernando S Carneiro ◽  
Rita C Tostes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Multiscale Entropy ◽  
Left Renal Artery ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
Interval Time Series ◽  
Electrocardiogram Ecg ◽  
2K1c Hypertension

Introduction: Hypertension is the most common chronic cardiovascular disease, being multifactorial in origin and an important cause of morbidity and mortality worldwide. Complex behaviors of heart rate series have been widely recognized and the loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. We hypothesized that two-kidney one clip (2K1C) hypertension reduces the HRV complexity in mice. Methods and Results: C57BL/6 mice were anesthetized with isoflurane and submitted to 2K1C hypertension by placing a silver clip (0.12 mm) around left renal artery. After 4 weeks, mice were implanted with subcutaneous electrocardiogram (ECG) electrodes and allowed to recover for 48 h. On the day of the experiment, the ECG was recorded for 30 minutes in conscious, unrestrained mice. At the end of the recording, arterial pressure (AP) was directly measured in each mouse under isoflurane anesthesia. RR interval time series were generated and the complexity of HRV was determined using detrending fluctuation analysis (DFA) and multiscale entropy (MSE). Mean AP was higher in 2K1C mice (133±2 vs 93±4 mmHg) while the HR was similar between groups. DFA scaling exponents were calculated in short (5 to 15), mid (30 to 200) and long (200 to 1500) window sizes, but only the long-term exponent was different between groups (1.27±0.09 vs 0.89±0.08 in 2K1C and sham mice, respectively). MSE was calculated up to scale 20 and averaged in short (1 to 5) and long (6 to 20) time scales. In both short (0.75±0.16 vs 1.25±0.11) and long (0.76±0.17 vs 1.22±0.09) ranges, entropy is lower in hypertensive mice. Conclusions: The complexity of HRV dynamics was found lower in renovascular hypertensive mice. Both sympathetic and vagal control of the heart seems to be involved in this process, as predictability (MSE) and fractality (DFA) is affected in various temporal scales. Nevertheless, the greatest entropy difference between groups is found at scale 6, which is closely related to respiration.

Detrending fluctuation analysis based on high-pass filtering

2007 ◽  
Vol 375 (2) ◽  
pp. 699-708 ◽  
Author(s):  
Eduardo Rodriguez ◽  
Juan Carlos Echeverría ◽  
Jose Alvarez-Ramirez
Keyword(s):  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
High Pass

MINIMIZING PERIODIC TRENDS BY APPLYING LAPLACE TRANSFORM

Fractals ◽  
2011 ◽  
Vol 19 (02) ◽  
pp. 203-211 ◽  
Author(s):  
AIJING LIN ◽  
PENGJIAN SHANG
Keyword(s):  
Time Series ◽  
Laplace Transform ◽  
Moving Average ◽  
Stationary Time Series ◽  
Fluctuation Analysis ◽  
Range Analysis ◽  
Long Range Correlations ◽  
Cross Correlation Analysis ◽  
Rescaled Range Analysis ◽  
Cross Correlations

Rescaled range analysis (R/S analysis), detrended fluctuation analysis (DFA) and detrended moving average (DMA) are widely-used methods for detection of long-range correlations in time series. Detrended cross-correlation analysis (DCCA) is a recently developed method to quantify the cross-correlations of two non-stationary time series. Another method for studying auto-correlations and cross-correlations was presented by Sergio Arianos and Anna Carbone in 2009. Recent studies have reported the susceptibility of this methods to periodic trends, which can result in spurious crossovers. In this paper, we propose the modified methods base on Laplace transform to minimizing the effect of periodic trends. The effectiveness of our techniques are demonstrated on stock data corrupted with periodic trends.

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