20 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…
Salvatore Miccichè
Many natural phenomena exhibit a stochastic nature that one attempts at modeling by using stochastic processes of different types. In this context, often one is interested in investigating the memory properties of the natural phenomenon at hand. This is usually accomplished by computing the autocorrelation function of…
Kellen Gong, Fang Li
The autocorrelation function (ACF) and partial autocorrelation function (PACF) are foundational tools for identifying autoregressive moving-average (ARMA) models, yet they are often introduced to students as computational recipes disconnected from the regression framework students already know. This note develops a…
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…
Min Yu, Hang-Hyun Jo
higher-order temporal correlations: An exponential case Authors: ['Min Yu' 'Hang-Hyun Jo'] Temporal correlations in the time series observed in various systems have been characterized by the autocorrelation function. Such correlations can be explained by heavy-tailed interevent time distributions as well as by…
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…
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…
Jing Zhang, Stefan Posse, Curtis Tatsuoka
Functional connectivity (FC) is the degree of synchrony of time series between distinct, spatially separated brain regions. While traditional FC analysis assumes the temporal stationarity throughout a brain scan, there is growing recognition that connectivity can change over time and is not stationary, leading to the…
Authors not listed
Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera's method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799–1805] is popular but has not been…
Gabriel Riegner, Samuel Davenport, Bradley Voytek, Armin Schwartzman
Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying autocorrelation and only…
Nils Damaschke, Volker Kühn, Holger Nobach
Signal processing of uniformly spaced data from stationary stochastic processes with missing samples is investigated. Besides randomly and independently occurring outliers also correlated data gaps are investigated. Non-parametric estimators for the mean value, the signal variance, the autocovariance and…
Authors not listed
Accurate and efficient simulation of vibrational Raman spectra for systems with strong anharmonicity and nuclear quantum effects remains challenging. Herein, we apply the recently developed constrained nuclear–electronic orbital (CNEO) framework to simulate Raman spectra. We implement analytic static polarizabilities…
Holger Nobach
To derive the auto-covariance function from a sampled and time-limited signal or the cross-covariance function from two such signals, the mean values must be estimated and removed from the signals. If no a priori information about the correct mean values is available and the mean values must be derived from the time…
Gabriel Riegner, Samuel Davenport, Bradley Voytek, Armin Schwartzman
Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying temporal autocorrelation…
Andraž Matkovič, Alan Anticevic, John D. Murray, Grega Repovš
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research…
Gabriel Riegner, Samuel Davenport, Bradley Voytek, Armin Schwartzman
Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying autocorrelation and only…
Alex E. Yuan, Wenying Shou
In disciplines from biology to climate science, a routine task is to compute a correlation between a pair of time series, and determine whether the correlation is statistically significant (i.e. unlikely under the null hypothesis that the time series are independent). This problem is challenging because time series…
Feiyu Jiang, Hanjia Gao, Xiaofeng Shao
We propose a novel method for testing serial independence of object-valued time series in metric spaces, which is more general than Euclidean or Hilbert spaces. The proposed method is fully nonparametric, free of tuning parameters and can capture all nonlinear pairwise dependence. The key concept used in this paper is…
Amelia Carolina Sparavigna
In an article by Thibault et al., 2002, we can find measurements of Raman linewidths in the Q branch of carbon monoxide, for mixtures with Argon at different temperatures. A plot is available for the Q(5) line with a fitted Voigt function. Here we show that a q-Gaussian Tsallis function can be used for fitting this…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…