15 papers · ranked by Valyu relevance
Pedro Carpena, Manuel Gómez-Extremera, Pedro A. Bernaola-Galván, Paolo Castiglioni
'Paolo Castiglioni'] Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function $F(ℓ)$, which quantifies the fluctuations of the time series around the local trend…
Yoshiki Kaneoke, Tomohiro Donishi, Jun Iwatani, Satoshi Ukai + 3 more
'Kazuhiro Shinosaki' 'Masaki Terada' 'Carles Soriano-Mas'] Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain…
F. P. Spitzner, J. Dehning, J. Wilting, A. Hagemann + 4 more
'J. Zierenberg' 'V. Priesemann' 'Michal Zochowski'] Here we present our Python toolbox “MR. Estimator” to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is…
Vincent Deblauwe, Pol Kennel, Pierre Couteron, Gil Bohrer
Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those…
Yanguang Chen, Bedilu Alamirie Ejigu
Generalizing spatial autocorrelation coefficients yields corresponding spatial autocorrelation functions. The spatial autocorrelation function can be regarded as a set of spatial autocorrelation coefficients . Spatial autocorrelation coefficients are determined by size measures and spatial proximity measures. A spatial…
Li Su, Michael J Daniels
In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive…
Martin Dodek, Eva Miklovičová, Arne Johannssen
This paper introduces a novel approach for the offline estimation of stationary moving average processes, further extending it to efficient online estimation of non-stationary processes. The novelty lies in a unique technique to solve the autocorrelation function matching problem leveraging that the autocorrelation…
Soroosh Afyouni, Stephen M. Smith, Thomas E. Nichols
The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's…
Christoph Bandt
The study of order patterns of three equally-spaced values $x_{t},x_{(t+d)},x_{(t+2d)}$ in a time series is a powerful tool. The lag d is changed in a wide range so that the differences of the frequencies of order patterns become autocorrelation functions. Similar to a spectrogram in speech analysis, four ordinal…
Johan Medrano, Abderrahmane Kheddar, Sofiane Ramdani
Correlation coefficients play a pivotal role in quantifying linear relationships between random variables. Yet, their application to time series data is very challenging due to temporal dependencies. This paper introduces a novel approach to estimate the statistical significance of correlation coefficients in time…
Aldana M González Montoro, Ricardo Cao, Nelson Espinosa, Javier Cudeiro + 1 more
'Javier Cudeiro' 'Jorge Mariño'] Background Pairwise association between neurons is a key feature in understanding neural coding. Statistical neuroscience provides tools to estimate and assess these associations. In the mammalian brain, activating ascending pathways arise from neuronal nuclei located at the brainstem…
Boumediene Hamzi, Marianne Clausel, Kamal Dingle, Marcus Hutter + 2 more
Spurious correlations between time series are a persistent problem: simple, low-complexity patterns are abundant, so unrelated series can easily exhibit high Pearson correlation. We argue that Kolmogorov complexity-a series’ resistance to compression-provides a principled diagnostic for flagging such cases. We prove an…
Raoul R. Nigmatullin, Vadim S. Alexandrov, Olga Korostynska
In the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the comparison of random…
Takashi Isogai
In this paper, a novel approach to building a dynamic correlation network of highly volatile financial asset returns is presented. Our method avoids the spurious correlation problem when estimating the dynamic correlation matrix of financial asset returns by using a filtering approach. A multivariate volatility model…
Edoardo Saccenti
In the scientific literature data analysis results are often presented when samples from different experiments or different conditions, technical replicates or times series are merged to increase the sample size before calculating the correlation coefficient. This way of proceeding violates two basic assumptions…