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Relevant bibliographies by topics / Time series analysis, Long range correlations

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Journal articles

Academic literature on the topic 'Time series analysis, Long range correlations'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Time series analysis, Long range correlations"

1

Sidorov, Sergei, Alexey Faizliev, and Vladimir Balash. "Measuring long-range correlations in news flow intensity time series." International Journal of Modern Physics C 28, no. 08 (2017): 1750103. http://dx.doi.org/10.1142/s0129183117501030.

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We consider the flow intensity of economic and financial news taken from a nine-month period of 2015. This data is found to be well approximated by a persistent self-affine walk. It is characterized by a Hurst exponent of [Formula: see text] over three orders of magnitude in time ranging from minutes to several days. In this paper, we use the Detrended Fluctuation Analysis (DFA) of order 1, Rescaled Range Analysis (R/S) and Fourier Transform Method (FTM) to examine long-range auto-correlation and self-similarity of time series of news flow intensity. DFA method allowed us to reveal a strong sc

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2

Fan, Jie, Wan Qing Li, Hong Zhang, and Ke Qiang Dong. "Return Intervals Analysis of the Sunspot Time Series." Applied Mechanics and Materials 29-32 (August 2010): 1144–49. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1144.

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Rescaled range analysis (R/S) method is a scaling method commonly used for detecting the long-range correlations in many time series. The aim of this paper is to show that, using the rescaled range analysis on sunspot time series, how the threshold values q affects the correlations of the return intervals for events above a certain threshold q. We find that both the original records and the return intervals are long-range correlated.

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3

Mariani, Maria C., Peter K. Asante, Md Al Masum Bhuiyan, Maria P. Beccar-Varela, Sebastian Jaroszewicz, and Osei K. Tweneboah. "Long-Range Correlations and Characterization of Financial and Volcanic Time Series." Mathematics 8, no. 3 (2020): 441. http://dx.doi.org/10.3390/math8030441.

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In this study, we use the Diffusion Entropy Analysis (DEA) to analyze and detect the scaling properties of time series from both emerging and well established markets as well as volcanic eruptions recorded by a seismic station, both financial and volcanic time series data have high frequencies. The objective is to determine whether they follow a Gaussian or Lévy distribution, as well as establish the existence of long-range correlations in these time series. The results obtained from the DEA technique are compared with the Hurst R/S analysis and Detrended Fluctuation Analysis (DFA) methodologi

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4

Yuan, Qianshun, Changgui Gu, Tongfeng Weng, and Huijie Yang. "Unbiased detrended fluctuation analysis: Long-range correlations in very short time series." Physica A: Statistical Mechanics and its Applications 505 (September 2018): 179–89. http://dx.doi.org/10.1016/j.physa.2018.03.043.

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5

CHAKRABORTI, A., and M. S. SANTHANAM. "FINANCIAL AND OTHER SPATIO-TEMPORAL TIME SERIES: LONG-RANGE CORRELATIONS AND SPECTRAL PROPERTIES." International Journal of Modern Physics C 16, no. 11 (2005): 1733–43. http://dx.doi.org/10.1142/s0129183105008230.

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In this paper, we review some of the properties of financial and other spatio-temporal time series generated from coupled map lattices, GARCH(1,1) processes and random processes (for which analytical results are known). We use the Hurst exponent (R/S analysis) and detrended fluctuation analysis as the tools to study the long-time correlations in the time series. We also compare the eigenvalue properties of the empirical correlation matrices, especially in relation to random matrices.

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6

Erzgräber, Hartmut, Fernanda Strozzi, José-Manuel Zaldívar, Hugo Touchette, Eugénio Gutiérrez, and David K. Arrowsmith. "Time series analysis and long range correlations of Nordic spot electricity market data." Physica A: Statistical Mechanics and its Applications 387, no. 26 (2008): 6567–74. http://dx.doi.org/10.1016/j.physa.2008.07.030.

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7

Shang, Pengjian, Yongbo Lu, and Santi Kamae. "Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis." Chaos, Solitons & Fractals 36, no. 1 (2008): 82–90. http://dx.doi.org/10.1016/j.chaos.2006.06.019.

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8

Cabrera-Brito, Laura, German Rodriguez, Luis García-Weil, Mercedes Pacheco, Esther Perez, and Joanna J. Waniek. "Fractal Analysis of Deep Ocean Current Speed Time Series." Journal of Atmospheric and Oceanic Technology 34, no. 4 (2017): 817–27. http://dx.doi.org/10.1175/jtech-d-16-0098.1.

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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 behavio

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9

Isvoran, Adriana, Laura Unipan, Dana Craciun, and Vasile Morariu. "Analysis of long-range correlation in sequences data of proteins." Journal of the Serbian Chemical Society 72, no. 4 (2007): 383–92. http://dx.doi.org/10.2298/jsc0704383i.

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The results presented here suggest the existence of correlations in the sequence data of proteins. 32 proteins, both globular and fibrous, both monomeric and polymeric, were analyzed. The primary structures of these proteins were treated as time series. Three spatial series of data for each sequence of a protein were generated from numerical correspondences between each amino acid and a physical property associated with it, i.e., its electric charge, its polar character and its dipole moment. For each series, the spectral coefficient, the scaling exponent and the Hurst coefficient were determi

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10

Ogata, H., K. Tokuyama, S. Nagasaka, et al. "Long-range Correlated Glucose Fluctuations in Diabetes." Methods of Information in Medicine 46, no. 02 (2007): 222–26. http://dx.doi.org/10.1055/s-0038-1625411.

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Summary Objectives : Our objective is to investigate diabetes- related alteration of glucose control in diurnal fluctuations in normal daily life by detrended fluctuation analysis (DFA). Methods : The fluctuations of glucose of 12 non-diabetic subjects and 15 diabetic patients were measured using a continuous glucose monitoring system (CGMS) over a period of one day. The glucose data was calculated by the DFA method, which is capable of revealing the presence of long-range correlations in time series with inherent non-stationarity. Results : Compared with the non-diabetic subjects, the mean gl

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