#Kspectra toolkit for mac os x#
#Kspectra toolkit software#
#Kspectra toolkit windows#

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Petersburg University 1, 3, SSA-MTM Toolkit for Spectral Analysis, kSpectra Toolkit 5.

#Kspectra toolkit software#

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#Kspectra toolkit for mac os x#

kSpectra Toolkit for Mac OS X from SpectraWorks.Necessary cookies are absolutely essential for the website to function properly.

Proceedings of 37th Meeting, Society of Exploration Geophysics, Oklahoma City. Digital spectral analysis with applications. X t = ∑ k = 1 M α k X t − k + ϵ k is the imaginary unit. Therefore, the maximum entropy method is equivalent to least-squares fitting the available time series data to an autoregressive model The maximum entropy rate stochastic process that satisfies the given empirical autocorrelation and variance constraints is an autoregressive model with independent and identically distributed zero-mean Gaussian input. Maximum entropy method attempts to improve the spectral resolution by extrapolating the correlation function beyond the maximum lag in such a way that the entropy of the corresponding probability density function is maximized in each step of the extrapolation. However, the window limits the spectral resolution. The window is applied to provide statistical stability as well as to avoid leakage from other parts of the spectrum. kspectra lite provides a set of powerful tools for analysis of univariate or multivariate time series in various sciences, ranging from electrical engineering and physics to geophysics, as well as life, biomedical sciences, finance and economics. In the periodogram approach to calculating the power spectra, the sample autocorrelation function is multiplied by some window function and then Fourier transformed. In such a case, inputting the known information allows the maximum entropy model to derive a better estimate of the center of the peak, thus improving spectral accuracy. For example, in spectral analysis the expected peak shape is often known, but in a noisy spectrum the center of the peak may not be clear.

In maximum entropy modeling, probability distributions are created on the basis of that which is known, leading to a type of statistical inference about the missing information which is called the maximum entropy estimate. The usefulness of the technique varies based on the source of the spectral data since it is dependent on the amount of assumed knowledge about the spectrum that can be applied to the model. It is simply the application of maximum entropy modeling to any type of spectrum and is used in all fields where data is presented in spectral form.

This assumption, which corresponds to the concept of maximum entropy as used in both statistical mechanics and information theory, is maximally non-committal with regard to the unknown values of the autocorrelation function of the time series. The method is based on choosing the spectrum which corresponds to the most random or the most unpredictable time series whose autocorrelation function agrees with the known values. The middle panel is a Multi-taper spectra of the method (MTM) implemented on the kSpectra Toolkit software package (three tapers and resolution of 2) on the. kSpectra Toolkit Using novel tools such as singular spectrum analysis and multitaper method, time series can be decomposed into noise and predictable components - trends and oscillatory modes, which can be reconstructed and forecast. The goal is to improve the spectral quality based on the principle of maximum entropy.

#Kspectra toolkit windows#

and recently I have used Analyseries, kSpectra and Timefrq43, moving from Dos to Windows to Mac. Maximum entropy spectral estimation is a method of spectral density estimation. Acycle provides data preparation tools, curve fitting.