scholarly journals Assessment of long-range cross-correlations in cardiorespiratory and cardiovascular interactions

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
Akio Nakata ◽  
Miki Kaneko ◽  
Chinami Taki ◽  
Naoko Evans ◽  
Taiki Shigematsu ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Moving Average ◽  
Time Lag ◽  
Higher Order ◽  
Cross Correlation Analysis ◽  
Challenges And Opportunities ◽  
Cross Correlations ◽  
Different Order

We propose higher-order detrending moving-average cross-correlation analysis (DMCA) to assess the long-range cross-correlations in cardiorespiratory and cardiovascular interactions. Although the original (zeroth-order) DMCA employs a simple moving-average detrending filter to remove non-stationary trends embedded in the observed time series, our approach incorporates a Savitzky–Golay filter as a higher-order detrending method. Because the non-stationary trends can adversely affect the long-range correlation assessment, the higher-order detrending serves to improve accuracy. To achieve a more reliable characterization of the long-range cross-correlations, we demonstrate the importance of the following steps: correcting the time scale, confirming the consistency of different order DMCAs, and estimating the time lag between time series. We applied this methodological framework to cardiorespiratory and cardiovascular time series analysis. In the cardiorespiratory interaction, respiratory and heart rate variability (HRV) showed long-range auto-correlations; however, no factor was shared between them. In the cardiovascular interaction, beat-to-beat systolic blood pressure and HRV showed long-range auto-correlations and shared a common long-range, cross-correlated factor. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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.

scholarly journals Connectedness between crude oil and US equities: The impact of COVID-19 pandemic

2021 ◽  
Author(s):  
Aktham Maghyereh ◽  
hussein abdoh
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Moving Average ◽  
Equity Market ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Impact ◽  
Dynamical Connection

Abstract In this paper, we exploit multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the impact of COVID-19 pandemic on the cross-correlations between oil and US equity market (as represented by the S&P 500 index). First, we examine the detrended moving average cross-correlation coefficient between oil and S&P 500 returns before and during the COVID-19 pandemic. The correlation analysis shows that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long range cross correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equity in the long range and a deeper dynamical connection between oil and US equity markets as indicated by the multifractality tests.

MULTIFRACTAL CROSS-CORRELATION ANALYSIS BASED ON STATISTICAL MOMENTS

Fractals ◽  
2012 ◽  
Vol 20 (03n04) ◽  
pp. 271-279 ◽  
Author(s):  
JING WANG ◽  
PENGJIAN SHANG ◽  
WEIJIE GE
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Moving Average ◽  
Statistical Moments ◽  
Comparative Performance ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Better Than

We introduce a new method, multifractal cross-correlation analysis based on statistical moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality between time series generated from complex system. Efficiency of this method is shown on multifractal series, comparing with the well-known multifractal detrended cross-correlation analysis (MFXDFA) and multifractal detrending moving average cross-correlation analysis (MFXDMA). We further apply this method on volatility time series of DJIA and NASDAQ indices, and find some interesting results. The MFSMXA has comparative performance with MFXDMA and sometimes perform slightly better than MFXDFA. Multifractal nature exists in volatility series. In addition, we find that the cross-multifractality of volatility series is mainly due to their cross-correlations, via comparing the MFSMXA results for original series with those for shuffled series.

CROSS-CORRELATIONS BETWEEN BACTERIAL FOODBORNE DISEASES AND METEOROLOGICAL FACTORS BASED ON MF-DCCA: A CASE IN SOUTH KOREA

Fractals ◽  
2020 ◽  
Vol 28 (03) ◽  
pp. 2050046 ◽  
Author(s):  
JIAN WANG ◽  
WEI SHAO ◽  
JUNSEOK KIM
Keyword(s):  
Time Series ◽  
South Korea ◽  
Long Range ◽  
Cross Correlation ◽  
Meteorological Factors ◽  
Foodborne Diseases ◽  
Long Range Correlations ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Series Pair

In this study, we apply multifractal detrended cross-correlation analysis (MF-DCCA) to examine the nonlinear cross-correlations between bacterial foodborne diseases (FBDs) and meteorological factors in South Korea. The results demonstrate that power-law cross-correlations between bacterial FBD and meteorological factors exist; and that multifractal characteristics are significant. In addition, the cross-correlation between bacterial FBD and temperature is more persistent than that between bacterial FBD and humidity. Comparison of the strengths of multifractal spectra showed that the degree of multifractality of the Humidity/FBD time series pair is greater than that of Temperature/FBD pair; this indicates that the monthly number of outpatient FBD cases is more sensitive to humidity. Furthermore, to document the major source of multifractality, we shuffle the original series. We conclude that both the long-range correlations and fat-tail distribution contribute to the multifractality of the Temperature/FBD time series pair. The long-range correlations are also an important source that contributes to the multifractality between bacterial FBD and humidity time series.

HIGHER ORDER COMPLEXITY OF TIME SERIES

2004 ◽  
Vol 14 (08) ◽  
pp. 2979-2990 ◽  
Author(s):  
FANJI GU ◽  
ENHUA SHEN ◽  
XIN MENG ◽  
YANG CAO ◽  
ZHIJIE CAI
Keyword(s):  
Time Series ◽  
Mental Arithmetic ◽  
Higher Order ◽  
Second Order ◽  
Stationary Time Series ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Logistic Mapping ◽  
Original Time ◽  
Different Order

A concept of higher order complexity is proposed in this letter. If a randomness-finding complexity [Rapp & Schmah, 2000] is taken as the complexity measure, the first-order complexity is suggested to be a measure of randomness of the original time series, while the second-order complexity is a measure of its degree of nonstationarity. A different order is associated with each different aspect of complexity. Using logistic mapping repeatedly, some quasi-stationary time series were constructed, the nonstationarity degree of which could be expected theoretically. The estimation of the second-order complexity of these time series shows that the second-order complexities do reflect the degree of nonstationarity and thus can be considered as its indicator. It is also shown that the second-order complexities of the EEG signals from subjects doing mental arithmetic are significantly higher than those from subjects in deep sleep or resting with eyes closed.

Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Long Range ◽  
Computer Networks ◽  
Fractal Analysis ◽  
High Speed ◽  
Long Range Dependence ◽  
Traffic Flows ◽  
Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

Indices of Total Stock Biomass in the "Northern" and Gulf of St. Lawrence Atlantic Cod (Gadus morhua) Stocks Derived from Time Series Analyses of Fixed Gear (Trap) Catches

1992 ◽  
Vol 49 (2) ◽  
pp. 202-209 ◽  
Author(s):  
G. A. Rose
Keyword(s):  
Time Series ◽  
Gadus Morhua ◽  
Atlantic Cod ◽  
Moving Average ◽  
Stock Biomass ◽  
Northern Cod ◽  
Cross Correlations ◽  
Time Series Analyses ◽  
Trap Catches ◽  
Total Stock

The hypothesis that annual catches of fixed gear fisheries are cross-correlated with stock biomass at lags predictable on the basis of the relative ages of fish comprising the catch and biomass was verified for the trapnet fisheries of the northeastern Newfoundland "northern" (NAFO 2J3KL) and northern "Gulf" of St. Lawrence (NAFO 3Pn4RS) Atlantic cod (Gadus morhua) stocks. Time series indices of stock biomass were derived from these cross-correlations. For northern cod, the index was a 3-yr weighted and lagged moving average of catch. For the years 1972–81 (the first half of the available data) the trap index (Ti) was regressed on the stock biomass (Bi) determined by sequential population models (SPA) (Ti = 0.477Bi0.638, r = 0.99, P < 0.01). Biomass forecasts for 1982–90 derived from this function (inverted) were positively correlated with recent SPA-based estimates (r = 0.94, P < 0.02). For Gulf cod, the index was a 4-yr weighted and lagged moving average of catch. This index was regressed on SPA-determined biomass for the years 1974–81 (Ti = −3.19 + 0.0217Bi, r = 0.99; P < 0.01). Biomass forecasts for 1982–90 were positively correlated with (but lower than) SPA-based biomass estimates for the Gulf stock (r = 0.91, P < 0.05).

MODELING CROSS-CORRELATIONS OF TRAFFIC FLOW

2010 ◽  
Vol 20 (10) ◽  
pp. 3323-3328 ◽  
Author(s):  
PENGJIAN SHANG ◽  
KEQIANG DONG ◽  
SANTI KAMAE
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Power Law ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Large Increment ◽  
Active Research

The study of diverse natural and nonstationary signals has recently become an area of active research for physicists. This is because these signals exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails and fractality. The focus of the present paper is on the intriguing power-law autocorrelations and cross-correlations in traffic series. Detrended Cross-Correlation Analysis (DCCA) is used to study the traffic flow fluctuations. It is demonstrated that the time series, observed on the Anhua-Bridge highway in the Beijing Third Ring Road (BTRR), may exhibit power-law cross-correlations when they come from two adjacent sections or lanes. This indicates that a large increment in one traffic variable is more likely to be followed by large increment in the other traffic variable. However, for traffic time series derived from nonadjacent sections or lanes, we find that even though they are power-law autocorrelated, there is no cross-correlation between them with a unique exponent. Our results show that DCCA techniques based on Detrended Fluctuation Analysis (DFA) can be used to analyze and interpret the traffic flow.

scholarly journals Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study

2021 ◽  
Author(s):  
Alessandro Rabiolo ◽  
Eugenio Alladio ◽  
Esteban Morales ◽  
Andrew I McNaught ◽  
Francesco Bandello ◽  
...  
Keyword(s):  
Time Series ◽  
Web Application ◽  
Moving Average ◽  
Time Lag ◽  
Search Behavior ◽  
Google Trends ◽  
Optimal Time ◽  
Time Range ◽  
Prediction Ability ◽  
Time Series Modelling

ABSTRACTBackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively).InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic.FundingEOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

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