Autocorrelation

autocorrelation functionserial correlationautocorrelatedauto-correlationautocorrelation matrixserial dependenceserial independenceserially correlatedserially uncorrelatedauto correlation
Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.wikipedia
293 Related Articles

Missing fundamental

even if the fundamental is not presentimplied fundamentalmissing fundamental frequency
The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies.
The precise way in which it does so is still a matter of debate, but the processing seems to be based on an autocorrelation involving the timing of neural impulses in the auditory nerve.

Autocovariance

autocovariance functionautocovariance matrixmean and autocovariance
In some fields, the term is used interchangeably with autocovariance.
Autocovariance is closely related to the autocorrelation of the process in question.

Wiener–Khinchin theorem

Wiener-Khinchin theoremWiener-Khintchine theoremWiener–Khinchin–Einstein theorem
The Wiener–Khinchin theorem relates the autocorrelation function to the power spectral density S_{XX} via the Fourier transform:
In applied mathematics, the Wiener–Khinchin theorem, also known as the Wiener–Khintchine theorem and sometimes as the Wiener–Khinchin–Einstein theorem or the Khinchin–Kolmogorov theorem, states that the autocorrelation function of a wide-sense-stationary random process has a spectral decomposition given by the power spectrum of that process.

White noise

whitenoisestatic
The autocorrelation of a continuous-time white noise signal will have a strong peak (represented by a Dirac delta function) at \tau=0 and will be exactly 0 for all other \tau.
In discrete time, white noise is a discrete signal whose samples are regarded as a sequence of serially uncorrelated random variables with zero mean and finite variance; a single realization of white noise is a random shock.

Cross-correlation

cross correlationcorrelationcorrelating
In an autocorrelation, which is the cross-correlation of a signal with itself, there will always be a peak at a lag of zero, and its size will be the signal energy.

Fourier transform

continuous Fourier transformFourierFourier transforms
The Wiener–Khinchin theorem relates the autocorrelation function to the power spectral density S_{XX} via the Fourier transform:
As a special case, the autocorrelation of function

Stationary process

stationarynon-stationarystationarity
If is a wide-sense stationary process then the mean \mu and the variance \sigma^2 are time-independent, and further the autocovariance function depends only on the lag between t_1 and t_2: the autocovariance depends only on the time-distance between the pair of values but not on their position in time.
This also implies that the autocorrelation depends only on, that is

Spectral density

frequency spectrumpower spectrumspectrum
The Wiener–Khinchin theorem relates the autocorrelation function to the power spectral density S_{XX} via the Fourier transform:
In the latter form (for a stationary random process), one can make the change of variables and with the limits of integration (rather than [0,T]) approaching infinity, the resulting power spectral density and the autocorrelation function of this signal are seen to be Fourier transform pairs (Wiener–Khinchin theorem).

Ordinary least squares

OLSleast squaresOrdinary least squares regression
In ordinary least squares (OLS), the adequacy of a model specification can be checked in part by establishing whether there is autocorrelation of the regression residuals.
The OLS estimator is consistent when the regressors are exogenous, and optimal in the class of linear unbiased estimators when the errors are homoscedastic and serially uncorrelated.

Durbin–Watson statistic

Durbin–Watson testDurbin–Watsonautocorrelated residuals
The traditional test for the presence of first-order autocorrelation is the Durbin–Watson statistic or, if the explanatory variables include a lagged dependent variable, Durbin's h statistic.
In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis.

Optical autocorrelation

autocorrelationoptical correlatorfemtosecond pulse measurement
In optics, various autocorrelation functions can be experimentally realized.

Vector autoregression

VARvector autoregressive modelstructural VAR estimation
With multiple interrelated data series, vector autoregression (VAR) or its extensions are used.

Autoregressive model

autoregressiveautoregressionAutoregressive process
Unit root processes, trend stationary processes, autoregressive processes, and moving average processes are specific forms of processes with autocorrelation.
The autocorrelation function of an AR(p) process can be expressed as :

Breusch–Godfrey test

A more flexible test, covering autocorrelation of higher orders and applicable whether or not the regressors include lags of the dependent variable, is the Breusch–Godfrey test.
In particular, it tests for the presence of serial correlation that has not been included in a proposed model structure and which, if present, would mean that incorrect conclusions would be drawn from other tests, or that sub-optimal estimates of model parameters are obtained if it is not taken into account.

Multivariate random variable

random vectorvectormultivariate
The auto-correlation matrix (also called second moment) of a random vector is an n \times n matrix containing as elements the autocorrelations of all pairs of elements of the random vector \mathbf{X}.
The correlation matrix (also called second moment) of an n \times 1 random vector is an n \times n matrix whose (i,j) th element is the correlation between the i th and the j th random variables.

Dynamic light scattering

DLSPhoton Correlation SpectroscopyDynamic Light Scattering (DLS)
is the autocorrelation function at a particular wave vector,

Newey–West estimator

Newey–West HAC estimatorNewey-West estimatorNewey–West
Responses to nonzero autocorrelation include generalized least squares and the Newey–West HAC estimator (Heteroskedasticity and Autocorrelation Consistent).
The estimator is used to try to overcome autocorrelation (also called serial correlation), and heteroskedasticity in the error terms in the models, often for regressions applied to time series data.

Fluorescence correlation spectroscopy

FCSfluorescence imaging
The resulting electronic signal can be stored either directly as an intensity versus time trace to be analyzed at a later point, or computed to generate the autocorrelation directly (which requires special acquisition cards).

Galton's problem

Galton's problem, named after Sir Francis Galton, is the problem of drawing inferences from cross-cultural data, due to the statistical phenomenon now called autocorrelation.

Time series

time series analysistime-seriestime-series analysis
It is common practice in some disciplines (e.g. statistics and time series analysis) to normalize the autocovariance function to get a time-dependent Pearson correlation coefficient.
The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis.

Correlogram

autocorrelation plotlag plotCorrelograms
For example, in time series analysis, a correlogram, also known as an autocorrelation plot, is a plot of the sample autocorrelations r_h\, versus h\, (the time lags).

Correlation function

correlationautocorrelation functioncorrelated
If one considers the correlation function between random variables representing the same quantity measured at two different points then this is often referred to as an autocorrelation function, which is made up of autocorrelations.

Triple correlation

The triple correlation extends the concept of autocorrelation, which correlates a function with a single shifted copy of itself and thereby enhances its latent periodicities.

Prais–Winsten estimation

Prais–Winsten transformationPrais and WinstenPrais–Winsten estimate
In econometrics, Prais–Winsten estimation is a procedure meant to take care of the serial correlation of type AR(1) in a linear model.

Cochrane–Orcutt estimation

Cochrane–Orcutt procedure
Cochrane–Orcutt estimation is a procedure in econometrics, which adjusts a linear model for serial correlation in the error term.