|Part of a series on Statistics|
|Correlation and covariance|
In probability theory and statistics, given a stochastic process, the autocovariance is a function that gives the covariance of the process with itself at pairs of time points. Autocovariance is closely related to the autocorrelation of the process in question.
Auto-covariance of stochastic processes
With the usual notation for the expectation operator, if the stochastic process has the mean function , then the autocovariance is given by: p. 162
where and are two instances in time.
Definition for weakly stationary process
If is a weakly stationary (WSS) process, then the following are true:: p. 163
- for all
- for all
where is the lag time, or the amount of time by which the signal has been shifted.
The autocovariance function of a WSS process is therefore given by:: p. 517
which is equivalent to
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. However in other disciplines (e.g. engineering) the normalization is usually dropped and the terms "autocorrelation" and "autocovariance" are used interchangeably.
The definition of the normalized auto-correlation of a stochastic process is
If the function is well-defined, its value must lie in the range , with 1 indicating perfect correlation and −1 indicating perfect anti-correlation.
For a WSS process, the definition is
- : p.169
respectively for a WSS process:
- : p.173
The autocovariance of a linearly filtered process
Calculating turbulent diffusivity
Autocovariance can be used to calculate turbulent diffusivity. Turbulence in a flow can cause the fluctuation of velocity in space and time. Thus, we are able to identify turbulence through the statistics of those fluctuations.
Reynolds decomposition is used to define the velocity fluctuations (assume we are now working with 1D problem and is the velocity along direction):
where is the true velocity, and is the expected value of velocity. If we choose a correct , all of the stochastic components of the turbulent velocity will be included in . To determine , a set of velocity measurements that are assembled from points in space, moments in time or repeated experiments is required.
If we assume the turbulent flux (, and c is the concentration term) can be caused by a random walk, we can use Fick's laws of diffusion to express the turbulent flux term:
The velocity autocovariance is defined as
where is the lag time, and is the lag distance.
The turbulent diffusivity can be calculated using the following 3 methods:
- If we have velocity data along a Lagrangian trajectory:
- If we have velocity data at one fixed (Eulerian) location:
- If we have velocity information at two fixed (Eulerian) locations:
- where is the distance separated by these two fixed locations.
Auto-covariance of random vectors
- Autoregressive process
- Noise covariance estimation (as an application example)
- ^ a b Hsu, Hwei (1997). Probability, random variables, and random processes. McGraw-Hill. ISBN 978-0-07-030644-8.
- ^ Lapidoth, Amos (2009). A Foundation in Digital Communication. Cambridge University Press. ISBN 978-0-521-19395-5.
- ^ a b Kun Il Park, Fundamentals of Probability and Stochastic Processes with Applications to Communications, Springer, 2018, 978-3-319-68074-3
- ^ Taylor, G. I. (1922-01-01). "Diffusion by Continuous Movements" (PDF). Proceedings of the London Mathematical Society. s2-20 (1): 196–212. doi:10.1112/plms/s2-20.1.196. ISSN 1460-244X.
- Hoel, P. G. (1984). Mathematical Statistics (Fifth ed.). New York: Wiley. ISBN 978-0-471-89045-4.
- Lecture notes on autocovariance from WHOI