scholarly journals Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 381
Author(s):  
Raoul Nigmatullin ◽  
Semyon Dorokhin ◽  
Alexander Ivchenko

In this paper, we focus on the generalization of the Hurst empirical law and suggest a set of reduced parameters for quantitative description of long-time series. These series are usually considered as a specific response of a complex system (economic, geophysical, electromagnetic and other systems), where successive fixations of external factors become impossible. We consider applying generalized Hurst laws to obtain a new set of reduced parameters in data associated with communication systems. We analyze three hypotheses. The first one contains one power-law exponent. The second one incorporates two power-law exponents, which are in many cases complex-conjugated. The third hypothesis has three power-law exponents, two of which are complex-conjugated as well. These hypotheses describe with acceptable accuracy (relative error does not exceed 2%) a wide set of trendless sequences (TLS) associated with radiometric measurements. Generalized Hurst laws operate with R/S curves not only in the asymptotic region, but in the entire domain. The fitting parameters can be used as the reduced parameters for the description of the given data. The paper demonstrates that this general approach can also be applied to other TLS.

1998 ◽  
Vol 5 (2) ◽  
pp. 93-104 ◽  
Author(s):  
D. Harris ◽  
M. Menabde ◽  
A. Seed ◽  
G. Austin

Abstract. The theory of scale similarity and breakdown coefficients is applied here to intermittent rainfall data consisting of time series and spatial rain fields. The probability distributions (pdf) of the logarithm of the breakdown coefficients are the principal descriptor used. Rain fields are distinguished as being either multiscaling or multiaffine depending on whether the pdfs of breakdown coefficients are scale similar or scale dependent, respectively. Parameter  estimation techniques are developed which are applicable to both multiscaling and multiaffine fields. The scale parameter (width), σ, of the pdfs of the log-breakdown coefficients is a measure of the intermittency of a field. For multiaffine fields, this scale parameter is found to increase with scale in a power-law fashion consistent with a bounded-cascade picture of rainfall modelling. The resulting power-law exponent, H, is indicative of the smoothness of the field. Some details of breakdown coefficient analysis are addressed and a theoretical link between this analysis and moment scaling analysis is also presented. Breakdown coefficient properties of cascades are also investigated in the context of parameter estimation for modelling purposes.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 112725-112734
Author(s):  
Wei Han ◽  
Zunjing Zhang ◽  
Chi Tang ◽  
Yili Yan ◽  
Erping Luo ◽  
...  

2021 ◽  
Author(s):  
Bora Shehu ◽  
Winfried Willems ◽  
Luisa Thiele ◽  
Henrike Stockel ◽  
Uwe Haberlandt

<p><span>Rainfall intensity-duration-frequency (IDF) curves are required for the design of several water systems and protection works. These curves are typically generated from the station data by fitting a theoretical distribution either to the annual extremes (AMS) or partial extremes (PE) series. Nevertheless, two main problems arise: i) for generating intensity depth for high return periods, long time series are needed (more than 40 years). While this is the case mainly for daily recordings, for sub-hourly time series only few point measurements are available. ii) as the station data are only local measurements, there is a need for regionalization of the of IDF curves to ungauged locations. Thus, the aim of this study is to investigate the use of different data types and methods in generating reliable IDF curves for ungauged locations. </span></p><p><span>For this purpose, the available gauge data from the German Weather Service (DWD) in Germany are employed, which include: 5000 daily stations with more than 40 years available, 1100 sub-hourly (5min) recordings with observations period shorter than 20 years, and finally 89 sub-hourly (5min) recordings with 60-70 years of observations. Annual extremes are extracted for each location for different durations D=5, 10, 15, 30, 60, 120, 180, 240, 360, 720, 2880 minutes, and a Generalized Extreme Value (GEV) probability distribution is fitted to each duration level as well as across all duration levels by the methods of the L-moments and Maximum-Likelihood, in order to derive the intensity quantiles for the given return periods Ta=2, 10, 20 and 100 years. First, a disaggregation scheme to 5 min resolution is performed on the daily recordings in order to investigate if disaggregated daily data can be useful for the IDF estimation of sub-daily durations. Then, the rainfall extremes of short observations are corrected by a correlation-based augmentation method. Finally, as the extreme intensities and durations are co-dependent, a normalization of the AMS over all the durations is performed.</span></p><p><span>To evaluate the regionalization of the IDF curves to ungauged regions, three methods are investigated: i) flood index method ii) regionalization with normalization of extremes over the durations and ii) kriging interpolation (ordinary and external drift kriging) of local AMS quantiles or parameters of the fitted distribution. The performance of these regionalization techniques is then evaluated by cross-validation, where the local IDF from the long sub-hourly time series are considered the true reference. Based on the relative bias, rmse and correlation the best method is selected and used for the regionalization of the IDF curves in Germany. Different data products are fed in the regionalization methods to answer the following questions: are the disaggregated long time series useful in regionalizing sub-hourly IDF? Can space be traded for time (and vice versa) when regionalizing IDF? What is the best incorporation of different data sets for the regionalization of the IDF? Lastly, a bootstrap method is as well employed to account for the uncertainties in estimation intensity-duration extremes for the given return periods. </span></p>


2005 ◽  
Vol 2005 (2) ◽  
pp. 111-117
Author(s):  
Juan R. Sánchez

The multiscale behavior of a recently reported model for stock markets is presented. It has been shown that indexes of real-world markets display absolute returns with memory properties on a long-time range, a phenomenon known as cluster volatility. The multiscale characteristics of an index are studied by analyzing the power-law scaling of the volatility correlations which display nonunique scaling exponents. Here such analysis is done on an artificial time series produced by a simple model for stock markets. After comparison, excellent agreements with the multiscale behavior of real-time series are found.


Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. Q11-Q19 ◽  
Author(s):  
Kosuke Chimoto ◽  
Hiroaki Yamanaka

For ambient noise, a long time series is typically used for measuring surface-wave dispersion in seismic interferometry. It is preferable to measure dispersions with a broad period range. The reliability of such measurements is often studied using the signal-to-noise ratio (S/N) of the crosscorrelation function (CCF). While many studies have revealed that the S/N evolves as the length of a time series increases, the required conditions for such measurements remain unclear. We maximized the period range suitable for dispersion measurements by examining variations in the amplitudes of the signals and noise of CCFs. For these purposes, and to preserve the broadband amplitude information, we do not apply filtering in the frequency domain or signal normalization in the time domain. The preserved signals and the trailing noise levels of the CCFs exhibit different time-varying features that agree with the predictions of theoretical work on amplitudes. Specifically, as the duration of the crosscorrelated time series increases, the amplitude of the signal remains constant while the trailing noise decreases. Moreover, the trailing noise exhibits a power-law dependence on the period. The period range in which the maximum CCF amplitude exceeds the level expected for this power law corresponds to the period range in which dispersion measurements can be made appropriately with frequency-time analysis (FTAN). This approach can be used to quantitatively determine the optimal period range for dispersion measurements. Results obtained with this method indicate that long-duration records used for crosscorrelation provide not only high S/Ns but also broaden the period range in which dispersion measurements can be made.


Fractals ◽  
1998 ◽  
Vol 06 (02) ◽  
pp. 101-108 ◽  
Author(s):  
Bruce J. West ◽  
Lori Griffin

The stride interval in normal human gait is not strictly constant, but fluctuates from step to step in a random manner. These fluctuations have traditionally been assumed to be uncorrelated random errors with normal statistics. Herein we show that, contrary to thes assumption these fluctuations have long-time correlations. Further, these long-time correlations are interpreted in terms of a scaling in the fluctuations indicating an allometric control process. To establish this result we measured the stride interval of a group of five healthy men and women as they walked for 5 to 15 minutes at their usual pace. From these time series we calculate the relative dispersion, the ratio of the standard deviation to the mean, and show by systematically aggregating the data that the correlation in the stride-interval time series is an inverse power law similar to the allometric relations in biology. The inverse power-law relative dispersion shows that the stride-interval time series scales indicating long-time self-similar correlations extending for hundreds of steps, which is to say that the underlying process is a random fractal. Furthermore, the power-law index is related to the fractal dimension of the time series. To determine if walking is a nonlinear process the stride-interval time series were randomly shuffled and the differences in the fractal dimensions of the surrogate time series from those of the original time series were determined to be statistically significant. This difference indicates the importance of the long-time correlations in walking.


2020 ◽  
Vol 21 (1) ◽  
pp. 102-117
Author(s):  
Novia Zalmita ◽  
Muhajirah Muhajirah ◽  
Abdul Wahab Abdi

One that influences human resource indicators is education. The teacher is a profession as a job of academic specialization in a relatively long time in college. Understanding related to teacher competence is very important to have by a prospective teacher because it can affect the quality of performance as a professional teacher. The teacher's competence is known as pedagogic, professional, social and personality competencies. The issue in this study is how the competency of the teacher of the Department of Geography Education FKIP Unsyiah as a prospective teacher of geography? The purpose of this study was to determine the competence of teachers in the Department of Geography Education FKIP Unsyiah as prospective geography teachers. Quantitative description approach is used in this study to find answers to the issue. The population in this study were students of the Department of Geography Education FKIP Unsyiah class of 2015 and 2016 who had been declared to have passed the Micro Teaching and Magang Kependidikan 3 course totaling 50 people. Because the population is small and can be reached, the determination of the sample using total sampling techniques so that the sample in this study is the whole population. Data collection is done by distributing test questions to respondents. The data was analyzed using the descriptive statistics percentage formula. The results of the study indicate that the level of teacher competence of Geography Education Department students as prospective teachers is in the moderate category, namely as many as 22 respondents (44%). A total of 12 respondents (24%) were in the high category, 15 respondents (30%) were in the low category and 1 respondent (2%) were in the very low category.


2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.


Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 352
Author(s):  
Janusz Miśkiewicz

Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.


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