Anti-correlation and multifractal features of Spain electricity spot market

Norouzzadeh, Payam; Dullaert, Wout; Rahmani, Bahareh

doi:10.1016/j.physa.2007.02.087

Cited by 68 publications

(57 citation statements)

References 6 publications

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“…Simonsen (2003) considered the Nordic electricity spot market, finding that the price differences are well approximated by a weak anti-persistent process 1 characterized by a Hurst exponent of 0.41 over three orders of magnitude in time ranging from days to years. Similar results were found for the Spain electricity market (Norouzzadeha et al, 2007). It should be emphasized that the above correlations studies focused on price differences rather than on absolute prices.…”

Section: Introductionsupporting

confidence: 80%

Time-dependent correlations in electricity markets

Alvarez-Ramı́rez

Escarela‐Perez

2010

Energy Economics

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Section: Introductionsupporting

confidence: 80%

Time-dependent correlations in electricity markets

Alvarez-Ramı́rez

Escarela‐Perez

2010

Energy Economics

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“…These were very close to one and quite similar to one another. These values reflected the presence of strong persistence or positive autocorrelations (Norouzzadeh et al, 2007) occurring in case of long-range dependencies. These correlation dependencies were also common in mono-fractal models such as the fractional Brownian motion model with Hurst exponents between one half and one (Riedi et al, 1999).…”

Section: Resultsmentioning

confidence: 93%

Multifractal analysis of discretized X-ray CT images for the characterization of soil macropore structures

et al. 2010

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Keywords:X-ray computed tomography of soil Image analysis Soil pore space Soil spatial variability Long-range dependence Multifractal analysis Hurst exponent A correct statistical model of soil pore structure can be critical for understanding flow and transport processes in soils, and creating synthetic soil pore spaces for hypothetical and model testing, and evaluating similarity of pore spaces of different soils. Advanced visualization techniques such as X-ray computed tomography (CT) offer new opportunities of exploring heterogeneity of soil properties at horizon or aggregate scales. Simple fractal models such as fractional Brownian motion that have been proposed to capture the complex behavior of soil spatial variation at field scale rarely simulate irregularity patterns displayed by spatial series of soil properties. The objective of this work was to use CT data to test the hypothesis that soil pore structure at the horizon scale may be represented by multifractal models. X-ray CT scans of twelve, water-saturated, 20-cm long soil columns with diameters of 7.5 cm were analyzed. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690 x 690 pixels. The images were binarized and the spatial series of the percentage of void space vs. depth was analyzed to evaluate the applicability of the multifractal model. The series of depth-dependent macroporosity values exhibited a welldefined multifractal structure that was revealed by singularity and Rényi spectra. The long-range dependencies in these series were parameterized by the Hurst exponent. Values of the Hurst exponent close to one were observed indicating the strong persistence in variations of porosity with depth. The multifractal modeling of soil macropore structure can be an efficient method for parameterizing and simulating the vertical spatial heterogeneity of soil pore space.

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“…The shuffling is repeated with different random seeds to avoid systematic errors in the random number generators. The algorithm of phase randomization (Norouzzadeh et al 2007;Small and Tse 2003) is determined by firstly taking the discrete Fourier transform of the time series, then shuffling the phases of the complex conjugate pairs (noting that the phases of complex numbers must be shuffled pairwise to preserve the realness of the inverse Fourier transformation) and finally, taking the inverse Fourier transformation. Ten different realizations of the shuffled and surrogate time series associated to the monthly drought area were generated in this way to reduce statistical errors.…”

Section: Sources Of Multifractalitymentioning

confidence: 99%

Multifractal analysis of the drought area in seven large regions of China from 1961 to 2012

Hou

Feng

Yan

et al. 2017

Meteorol Atmos Phys

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Based on a monthly drought index, the Standardized Precipitation Index (SPI), we investigate the multifractality of monthly drought areas in seven major regions in China from 1961 to 2012 using multifractal detrended fluctuation analysis. The results show that multifractality is evident in the monthly time series for all seven regions, but its strength varies between areas. From the numerical results, we further examine the stationarity and persistence of the time series in the seven drought areas. The characteristics of the variance of big and small fluctuations are also analyzed. The characteristics of multifractal spectra are used to distinguish the features of the singularity of all the data, as well as the large and small fluctuations and the spread of the changes of fractal patterns, and so on, in the monthly drought area time series for the different regions. Finally, we investigate the possible source(s) of multifractality in the drought series by random shuffling as well as surrogating the original series for each region.

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Anti-correlation and multifractal features of Spain electricity spot market

Cited by 68 publications

References 6 publications

Time-dependent correlations in electricity markets

Time-dependent correlations in electricity markets

Multifractal analysis of discretized X-ray CT images for the characterization of soil macropore structures

Multifractal analysis of the drought area in seven large regions of China from 1961 to 2012

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