scholarly journals Fluctuation Characteristics of Water Level and Water Temperature of Huize Well Based on MF-DCCA

2021 ◽  
Vol 39 (3) ◽  
pp. 825-832
Author(s):  
Jian Yu ◽  
Lili Sui ◽  
Yirong Xu ◽  
Baoming Chi
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Water Level ◽  
Cross Correlation ◽  
Correlation Method ◽  
Multifractal Spectrum ◽  
Earthquake Precursor ◽  
Observation Data ◽  
Cross Correlation Analysis ◽  
Abnormal Increase

In recent decades, the network of seismic subsurface fluid observatories is developing constantly, the observation data of subsurface fluids are enriched accordingly, which provides a favorable condition for the research on the formation, occurrence, and development of earthquakes. In the observation data of subsurface fluids, water level and water temperature changes are very important observation indicators, and their fluctuation sequences are quite complicated. Therefore, this paper employed a non-linear cross-correlation method to study the relationship between the water level and water temperature of Huize Well from 2004 to 2006, and found that there’s a significant cross-correlation between the time series of water level and water temperature; then, this study adopted DCCA (detrended cross-correlation analysis) to calculate the cross-correlation coefficient under different scales and explore the continuous changes of water level and water temperature; at last, this paper used the MF-DCCA (Multifractal-DCCA) method to prove that there’s multifractal cross-correlation between the time series of water level and water temperature. Before the M5.1 earthquake in Huize area, there’s an abnormal increase in the width of the multifractal spectrum of the water level and water temperature drawn with a sliding window of 500-hour, and this is a possible earthquake precursor.

scholarly journals Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1116
Author(s):  
Adarsh Sankaran ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Archana Devarajan Sindhu ◽  
Nandhineekrishna Kumar ◽  
...  
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Reference Evapotranspiration ◽  
Multifractal Spectrum ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Cross Correlation Analysis ◽  
The Individual

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

scholarly journals Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 298
Author(s):  
Zhiyan Zhao ◽  
Bin Wu ◽  
Ting Zhou
Keyword(s):  
Cross Correlation ◽  
Wavelet Packet ◽  
Reference Signal ◽  
Correlation Method ◽  
Health Condition ◽  
Wavelet Packet Transform ◽  
Rolling Stock ◽  
Health State ◽  
Vibration Signals ◽  
Cross Correlation Analysis

The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%.

scholarly journals Measurements of solar internal rotation obtained with the Mt. Wilson 60-foot solar tower

1988 ◽  
Vol 123 ◽  
pp. 41-44
Author(s):  
Edward J. Rhodes ◽  
Alessandro Cacciani ◽  
Martin Woodard ◽  
Steven Tomczyk ◽  
Sylvain Korzennik ◽  
...  
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Rotational Velocity ◽  
Optical Filter ◽  
Power Spectra ◽  
Mean Value ◽  
Current Limit ◽  
Surface Rotation ◽  
Cross Correlation Analysis ◽  
Full Disk

We have obtained estimates of the solar internal rotational velocity from measurements of the frequency splittings of p-mode oscillations. Specifically, we have analyzed a 10-day time series of full-disk Dopplergrams obtained during July and August 1984 at the 60-Foot Tower Telescope of the Mt. Wilson Observatory. The Dopplergrams were obtained with a Na magneto-optical filter and a 244 × 248-pixel CID camera. From the time series we computed power spectra for all of the prograde and retrograde sectoral p-modes from ℓ = 0 to 200 and for all of the tessaral harmonics up to ℓ = 89. We then applied a cross-correlation analysis to the resulting sectoral power spectra to obtain estimates of the frequency splittings. From ℓ = 4 to ℓ = 30 we obtained a mean value of the frequency spitting of roughly 450 nHz (sidereal) in close agreement with most previously published results, while from ℓ = 40 to ℓ = 140 we obtained a mean value of about 470 nHz. We believe that the latter value is slightly higher than the surface rotational splitting of 461 nHz because of possible confusion due to the temporal sidelobes introduced by the day/night observing cycle. Confirmation of this possibility will have to await our computation of tesseral power spectra for degrees greater than our current limit of 89. Finally, for degrees between 140 and 200, the frequency splittings are indistinguishable from the surface rotation rate.

Wavelet Cross-Correlation Analysis of the Unsteady Signals of the Flow in the Open Sump

Volume 1 ◽  
2004 ◽  
Author(s):  
Mansa Kante ◽  
Yulin Wu ◽  
Yong Li ◽  
Shuhong Liu ◽  
Daqing Zhou
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Vibration Signal ◽  
Structural Similarity ◽  
Signal Distribution ◽  
Cross Correlation Analysis ◽  
Correlation Scale ◽  
Pump Sump ◽  
The Relationship ◽  
Signal Analysis Method

The wavelet cross-correlation method was used to analyze the unsteady signals of the flow of the model open pump sump, which include pressure signal, vibration signal and acoustic signal. The continuous wavelet transform was done first to find the signal distribution at various periods and at any time, then the wavelet cross-correlation was used to find the relationship between the signals taken two a two. Through comparing the result of wavelet cross-correlation and the result of classic cross-correlation, one can find the correlation scale of any two unsteady signals (pressure-vibration, pressure-noise, and vibration-noise). The signal on the correlation scale was reconstruct and its characteristics were obtained using classical signal analysis method same as the structural similarity of a arbitrary two signals.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

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.

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