The correlation between data in one part of an image or at one time and that at another. The spatial autocorrelation in an image can be measured by Fourier Transforms and Variograms.

The correlation of a variable with itself over successive time intervals.

The autocorrelation of an object , known as the Patterson map in crystallography, is the correlation product of with itself: A â€¢ A . By the convolution theorem, the Fourier transform of the autocorrelation of is the squared Fourier modulus of : F( A â€¢ A ) = F(A)* F(A) = |F(A)| 2. The autocorrelation is therefore an equivalent way of representing Fourier modulus data.

The correlation between adjacent observations in time or space.

Multiplication of a signal with a time-delayed replica of itself.

spectral analysis tool that extracts frequencies by determining the degree of similarity between two curves. Since two successive glottal pulses in a speech signal look very similar (and thus have similar numeric values), the correlation between them is very high. F0 is determined from the strongest correlation peak found in a series of complarisons of successive signal segments. Advantages include reliability over a wide range of conditions, including noisy background. Disadvanatages include low calculation speed and smoothing of rapid voice modulations.

The correlation of a signal with a delayed version of itself.

Is the statistical dependency of items within a time series. This compares to Serial Correlation.

The correlation between the values of a time series and previous values of the same series.

A correlation between a component of a stochastic process and itself lagged a certain period of time.

When observations are correlated over time. In other words, the covariance between data recorded on the same series sequentially in time is nonzero.

The simple linear correlation of a time series with its own past; that is, the correlation of the sequence of values () with the sequence of values ( + Ï„) occurring Ï„ units of time later. The time displacement Ï„ is called the lag. The autocorrelation function is the autocorrelation for variable lag. The autocorrelation coefficient is the product-moment correlation coefficient that relates the variables () and ( See serial correlation.

is a type of serial correlation and is the correlation (usually linear-squared correlation) between members of a time series of observations, and the same values at a fixed time interval later. [1

A term referring to the correlation of a time series with a lagged version of itself. Sometimes referred to as serial correlation or lagged correlation.

Correlation of the error terms from different observations of the same variable. Also called serial correlation.

Autocorrelation is a mathematical tool used frequently in signal processing for analysing functions or series of values, such as time domain signals. Informally, it is a measure of how well a signal matches a time-shifted version of itself, as a function of the amount of time shift. More precisely, it is the cross-correlation of a signal with itself.