Autocorrelation

Part of a series on Statistics
Correlation and covariance
For random vectors Autocorrelation matrix Cross-correlation matrix Auto-covariance matrix Cross-covariance matrix
For stochastic processes Autocorrelation function Cross-correlation function Autocovariance function Cross-covariance function
For deterministic signals Autocorrelation function Cross-correlation function Autocovariance function Cross-covariance function
v t e

sine function. Below: The sine function revealed in a correlogram

produced by autocorrelation.

Autocorrelation, sometimes known as serial correlation in the

signals.

Different fields of study define autocorrelation differently, and not all of these definitions are equivalent. In some fields, the term is used interchangeably with autocovariance.

are specific forms of processes with autocorrelation.

Auto-correlation of stochastic processes

In

between values of the process at different times, as a function of the two times or of the time lag. Let ${\displaystyle \left\{X_{t}\right\}}$ be a random process, and ${\displaystyle t}$ be any point in time (${\displaystyle t}$ may be an

continuous-time

process). Then ${\displaystyle X_{t}}$ is the value (or realization) produced by a given run of the process at time ${\displaystyle t}$. Suppose that the process has mean ${\displaystyle \mu _{t}}$ and variance ${\displaystyle \sigma _{t}^{2}}$ at time ${\displaystyle t}$, for each ${\displaystyle t}$. Then the definition of the auto-correlation function between times ${\displaystyle t_{1}}$ and ${\displaystyle t_{2}}$ is^{[1]: p.388 [2]: p.165}

where ${\displaystyle \operatorname {E} }$ is the

.

Subtracting the mean before multiplication yields the auto-covariance function between times ${\displaystyle t_{1}}$ and ${\displaystyle t_{2}}$:^{[1]: p.392 [2]: p.168}

Note that this expression is not well defined for all time series or processes, because the mean may not exist, or the variance may be zero (for a constant process) or infinite (for processes with distribution lacking well-behaved moments, such as certain types of power law).

Definition for wide-sense stationary stochastic process

If ${\displaystyle \left\{X_{t}\right\}}$ is a

then the mean ${\displaystyle \mu }$ and the variance ${\displaystyle \sigma ^{2}}$ are time-independent, and further the autocovariance function depends only on the lag between ${\displaystyle t_{1}}$ and ${\displaystyle t_{2}}$: the autocovariance depends only on the time-distance between the pair of values but not on their position in time. This further implies that the autocovariance and auto-correlation can be expressed as a function of the time-lag, and that this would be an of the lag ${\displaystyle \tau =t_{2}-t_{1}}$. This gives the more familiar forms for the auto-correlation function^[1]: p.395

and the auto-covariance function:

In particular, note that

Normalization

It is common practice in some disciplines (e.g. statistics and

. 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 auto-correlation coefficient of a stochastic process is[2]^: p.169

If the function ${\displaystyle \rho _{XX}}$ is well defined, its value must lie in the range ${\displaystyle [-1,1]}$, with 1 indicating perfect correlation and −1 indicating perfect

.

For a wide-sense stationary (WSS) process, the definition is

.

The normalization is important both because the interpretation of the autocorrelation as a correlation provides a scale-free measure of the strength of

, and because the normalization has an effect on the statistical properties of the estimated autocorrelations.

Properties

Symmetry property

The fact that the auto-correlation function ${\displaystyle \operatorname {R} _{XX}}$ is an

^: p.171

$${\displaystyle \operatorname {R} _{XX}(t_{1},t_{2})={\overline {\operatorname {R} _{XX}(t_{2},t_{1})}}}$$

respectively for a WSS process:[2]^: p.173

Maximum at zero

For a WSS process:^[2]: p.174

Notice that ${\displaystyle \operatorname {R} _{XX}(0)}$ is always real.

Cauchy–Schwarz inequality

The Cauchy–Schwarz inequality, inequality for stochastic processes:^[1]: p.392

Autocorrelation of white noise

The autocorrelation of a continuous-time white noise signal will have a strong peak (represented by a Dirac delta function) at ${\displaystyle \tau =0}$ and will be exactly ${\displaystyle 0}$ for all other ${\displaystyle \tau }$.

Wiener–Khinchin theorem

The Wiener–Khinchin theorem relates the autocorrelation function ${\displaystyle \operatorname {R} _{XX}}$ to the power spectral density ${\displaystyle S_{XX}}$ via the Fourier transform:

For real-valued functions, the symmetric autocorrelation function has a real symmetric transform, so the Wiener–Khinchin theorem can be re-expressed in terms of real cosines only:

Auto-correlation of random vectors

The (potentially time-dependent) auto-correlation matrix (also called second moment) of a (potentially time-dependent)

${\displaystyle \mathbf {X} =(X_{1},\ldots ,X_{n})^{\rm {T}}}$ is an ${\displaystyle n\times n}$ matrix containing as elements the autocorrelations of all pairs of elements of the random vector ${\displaystyle \mathbf {X} }$. The autocorrelation matrix is used in various digital signal processing algorithms.

For a

${\displaystyle \mathbf {X} =(X_{1},\ldots ,X_{n})^{\rm {T}}}$ containing random elements whose expected value and variance exist, the auto-correlation matrix is defined by^{[3]: p.190 [1]: p.334}

where ${\displaystyle {}^{\rm {T}}}$ denotes the transposed matrix of dimensions ${\displaystyle n\times n}$.

Written component-wise:

If ${\displaystyle \mathbf {Z} }$ is a complex random vector, the autocorrelation matrix is instead defined by

Here ${\displaystyle {}^{\rm {H}}}$ denotes

.

For example, if ${\displaystyle \mathbf {X} =\left(X_{1},X_{2},X_{3}\right)^{\rm {T}}}$ is a random vector, then ${\displaystyle \operatorname {R} _{\mathbf {X} \mathbf {X} }}$ is a ${\displaystyle 3\times 3}$ matrix whose ${\displaystyle (i,j)}$-th entry is ${\displaystyle \operatorname {E} [X_{i}X_{j}]}$.

Properties of the autocorrelation matrix

• The autocorrelation matrix is a Hermitian matrix for complex random vectors and a symmetric matrix for real random vectors.^[3]: p.190
• The autocorrelation matrix is a
  positive semidefinite matrix,^[3]
  ^: p.190 i.e. ${\displaystyle \mathbf {a} ^{\mathrm {T} }\operatorname {R} _{\mathbf {X} \mathbf {X} }\mathbf {a} \geq 0\quad {\text{for all }}\mathbf {a} \in \mathbb {R} ^{n}}$ for a real random vector, and respectively ${\displaystyle \mathbf {a} ^{\mathrm {H} }\operatorname {R} _{\mathbf {Z} \mathbf {Z} }\mathbf {a} \geq 0\quad {\text{for all }}\mathbf {a} \in \mathbb {C} ^{n}}$ in case of a complex random vector.
• All eigenvalues of the autocorrelation matrix are real and non-negative.
• The auto-covariance matrix is related to the autocorrelation matrix as follows:
  $${\displaystyle \operatorname {K} _{\mathbf {X} \mathbf {X} }=\operatorname {E} [(\mathbf {X} -\operatorname {E} [\mathbf {X} ])(\mathbf {X} -\operatorname {E} [\mathbf {X} ])^{\rm {T}}]=\operatorname {R} _{\mathbf {X} \mathbf {X} }-\operatorname {E} [\mathbf {X} ]\operatorname {E} [\mathbf {X} ]^{\rm {T}}}$$

  
  Respectively for complex random vectors:
  $${\displaystyle \operatorname {K} _{\mathbf {Z} \mathbf {Z} }=\operatorname {E} [(\mathbf {Z} -\operatorname {E} [\mathbf {Z} ])(\mathbf {Z} -\operatorname {E} [\mathbf {Z} ])^{\rm {H}}]=\operatorname {R} _{\mathbf {Z} \mathbf {Z} }-\operatorname {E} [\mathbf {Z} ]\operatorname {E} [\mathbf {Z} ]^{\rm {H}}}$$

  

Auto-correlation of deterministic signals

In signal processing, the above definition is often used without the normalization, that is, without subtracting the mean and dividing by the variance. When the autocorrelation function is normalized by mean and variance, it is sometimes referred to as the autocorrelation coefficient^[4] or autocovariance function.

Auto-correlation of continuous-time signal

Given a

${\displaystyle f(t)}$, the continuous autocorrelation ${\displaystyle R_{ff}(\tau )}$ is most often defined as the continuous cross-correlation integral of ${\displaystyle f(t)}$ with itself, at lag ${\displaystyle \tau }$.^[1]: p.411

where ${\displaystyle {\overline {f(t)}}}$ represents the complex conjugate of ${\displaystyle f(t)}$. Note that the parameter ${\displaystyle t}$ in the integral is a dummy variable and is only necessary to calculate the integral. It has no specific meaning.

Auto-correlation of discrete-time signal

The discrete autocorrelation ${\displaystyle R}$ at lag ${\displaystyle \ell }$ for a discrete-time signal ${\displaystyle y(n)}$ is

The above definitions work for signals that are square integrable, or square summable, that is, of finite energy. Signals that "last forever" are treated instead as random processes, in which case different definitions are needed, based on expected values. For

, the autocorrelations are defined as

For processes that are not stationary, these will also be functions of ${\displaystyle t}$, or ${\displaystyle n}$.

For processes that are also ergodic, the expectation can be replaced by the limit of a time average. The autocorrelation of an ergodic process is sometimes defined as or equated to^[4]

These definitions have the advantage that they give sensible well-defined single-parameter results for periodic functions, even when those functions are not the output of stationary ergodic processes.

Alternatively, signals that last forever can be treated by a short-time autocorrelation function analysis, using finite time integrals. (See short-time Fourier transform for a related process.)

Definition for periodic signals

If ${\displaystyle f}$ is a continuous periodic function of period ${\displaystyle T}$, the integration from ${\displaystyle -\infty }$ to ${\displaystyle \infty }$ is replaced by integration over any interval ${\displaystyle [t_{0},t_{0}+T]}$ of length ${\displaystyle T}$:

which is equivalent to

Properties

In the following, we will describe properties of one-dimensional autocorrelations only, since most properties are easily transferred from the one-dimensional case to the multi-dimensional cases. These properties hold for wide-sense stationary processes.^[5]

• A fundamental property of the autocorrelation is symmetry, ${\displaystyle R_{ff}(\tau )=R_{ff}(-\tau )}$, which is easy to prove from the definition. In the continuous case,
  • the autocorrelation is an
    even function
    ${\displaystyle R_{ff}(-\tau )=R_{ff}(\tau )}$ when ${\displaystyle f}$ is a real function, and
  • the autocorrelation is a Hermitian function ${\displaystyle R_{ff}(-\tau )=R_{ff}^{*}(\tau )}$ when ${\displaystyle f}$ is a
    complex function
    .
• The continuous autocorrelation function reaches its peak at the origin, where it takes a real value, i.e. for any delay ${\displaystyle \tau }$, ${\displaystyle |R_{ff}(\tau )|\leq R_{ff}(0)}$.^[1]: p.410 This is a consequence of the rearrangement inequality. The same result holds in the discrete case.
• The autocorrelation of a periodic function is, itself, periodic with the same period.
• The autocorrelation of the sum of two completely uncorrelated functions (the cross-correlation is zero for all ${\displaystyle \tau }$) is the sum of the autocorrelations of each function separately.
• Since autocorrelation is a specific type of cross-correlation, it maintains all the properties of cross-correlation.
• By using the symbol ${\displaystyle *}$ to represent convolution and ${\displaystyle g_{-1}}$ is a function which manipulates the function ${\displaystyle f}$ and is defined as ${\displaystyle g_{-1}(f)(t)=f(-t)}$, the definition for ${\displaystyle R_{ff}(\tau )}$ may be written as:
  $${\displaystyle R_{ff}(\tau )=(f*g_{-1}({\overline {f}}))(\tau )}$$

  

Multi-dimensional autocorrelation

Multi-

would be

When mean values are subtracted from signals before computing an autocorrelation function, the resulting function is usually called an auto-covariance function.

Efficient computation

For data expressed as a

based on the signal processing definition ${\displaystyle R_{xx}(j)=\sum _{n}x_{n}\,{\overline {x}}_{n-j}}$ can be used when the signal size is small. For example, to calculate the autocorrelation of the real signal sequence ${\displaystyle x=(2,3,-1)}$ (i.e. ${\displaystyle x_{0}=2,x_{1}=3,x_{2}=-1}$, and ${\displaystyle x_{i}=0}$ for all other values of i) by hand, we first recognize that the definition just given is the same as the "usual" multiplication, but with right shifts, where each vertical addition gives the autocorrelation for particular lag values:

Thus the required autocorrelation sequence is ${\displaystyle R_{xx}=(-2,3,14,3,-2)}$, where ${\displaystyle R_{xx}(0)=14,}$ ${\displaystyle R_{xx}(-1)=R_{xx}(1)=3,}$ and ${\displaystyle R_{xx}(-2)=R_{xx}(2)=-2,}$ the autocorrelation for other lag values being zero. In this calculation we do not perform the carry-over operation during addition as is usual in normal multiplication. Note that we can halve the number of operations required by exploiting the inherent symmetry of the autocorrelation. If the signal happens to be periodic, i.e. ${\displaystyle x=(\ldots ,2,3,-1,2,3,-1,\ldots ),}$ then we get a circular autocorrelation (similar to circular convolution) where the left and right tails of the previous autocorrelation sequence will overlap and give ${\displaystyle R_{xx}=(\ldots ,14,1,1,14,1,1,\ldots )}$ which has the same period as the signal sequence ${\displaystyle x.}$ The procedure can be regarded as an application of the convolution property of Z-transform of a discrete signal.

While the brute force algorithm is order n², several efficient algorithms exist which can compute the autocorrelation in order n log(n). For example, the Wiener–Khinchin theorem allows computing the autocorrelation from the raw data X(t) with two fast Fourier transforms (FFT):^{[6][page needed]}

where IFFT denotes the inverse fast Fourier transform. The asterisk denotes complex conjugate.

Alternatively, a multiple τ correlation can be performed by using brute force calculation for low τ values, and then progressively binning the X(t) data with a logarithmic density to compute higher values, resulting in the same n log(n) efficiency, but with lower memory requirements.^[7][8]

Estimation

For a

process with known mean and variance for which we observe ${\displaystyle n}$ observations ${\displaystyle \{X_{1},\,X_{2},\,\ldots ,\,X_{n}\}}$, an estimate of the autocorrelation coefficient may be obtained as

for any positive integer ${\displaystyle k<n}$. When the true mean ${\displaystyle \mu }$ and variance ${\displaystyle \sigma ^{2}}$ are known, this estimate is

of the process are not known there are several possibilities:

• If ${\displaystyle \mu }$ and ${\displaystyle \sigma ^{2}}$ are replaced by the standard formulae for sample mean and sample variance, then this is a
  biased estimate
  .
• A periodogram-based estimate replaces ${\displaystyle n-k}$ in the above formula with ${\displaystyle n}$. This estimate is always biased; however, it usually has a smaller mean squared error.^[9][10]
• Other possibilities derive from treating the two portions of data ${\displaystyle \{X_{1},\,X_{2},\,\ldots ,\,X_{n-k}\}}$ and ${\displaystyle \{X_{k+1},\,X_{k+2},\,\ldots ,\,X_{n}\}}$ separately and calculating separate sample means and/or sample variances for use in defining the estimate.^{[citation needed]}

The advantage of estimates of the last type is that the set of estimated autocorrelations, as a function of ${\displaystyle k}$, then form a function which is a valid autocorrelation in the sense that it is possible to define a theoretical process having exactly that autocorrelation. Other estimates can suffer from the problem that, if they are used to calculate the variance of a linear combination of the ${\displaystyle X}$'s, the variance calculated may turn out to be negative.^[11]

Regression analysis

In

(VAR) or its extensions are used.

In

t-scores

overestimated) when the autocorrelations of the errors at low lags are positive.

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. The Durbin-Watson can be linearly mapped however to the Pearson correlation between values and their lags.^[12] 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. This involves an auxiliary regression, wherein the residuals obtained from estimating the model of interest are regressed on (a) the original regressors and (b) k lags of the residuals, where 'k' is the order of the test. The simplest version of the test statistic from this auxiliary regression is TR², where T is the sample size and R² is the coefficient of determination. Under the null hypothesis of no autocorrelation, this statistic is asymptotically distributed as ${\displaystyle \chi ^{2}}$ with k degrees of freedom.

Responses to nonzero autocorrelation include

In the estimation of a

moving average model

(MA), the autocorrelation function is used to determine the appropriate number of lagged error terms to be included. This is based on the fact that for an MA process of order q, we have ${\displaystyle R(\tau )\neq 0}$, for ${\displaystyle \tau =0,1,\ldots ,q}$, and ${\displaystyle R(\tau )=0}$, for ${\displaystyle \tau >q}$.

Applications

Autocorrelation's ability to find repeating patterns in data yields many applications, including:

• Autocorrelation analysis is used heavily in fluorescence correlation spectroscopy^[14] to provide quantitative insight into molecular-level diffusion and chemical reactions.^[15]
• Another application of autocorrelation is the measurement of
  optical spectra and the measurement of very-short-duration light pulses produced by lasers, both using optical autocorrelators
  .
• Autocorrelation is used to analyze
  speckle pattern
  that results from the motion of the particles. Autocorrelation of the signal can be analyzed in terms of the diffusion of the particles. From this, knowing the viscosity of the fluid, the sizes of the particles can be calculated.
• Utilized in the
  doppler shift in the incoming satellite signal, until the receiver replica signal and the satellite signal codes match up.^[16]
• The small-angle X-ray scattering intensity of a nanostructured system is the Fourier transform of the spatial autocorrelation function of the electron density.
• In surface science and scanning probe microscopy, autocorrelation is used to establish a link between surface morphology and functional characteristics.^[17]
• In optics, normalized autocorrelations and cross-correlations give the
  degree of coherence
  of an electromagnetic field.
• In astronomy, autocorrelation can determine the frequency of pulsars.
• In music, autocorrelation (when applied at time scales smaller than a second) is used as a pitch detection algorithm for both instrument tuners and "Auto Tune" (used as a distortion effect or to fix intonation).^[18] When applied at time scales larger than a second, autocorrelation can identify the musical beat, for example to determine tempo.
• Autocorrelation in space rather than time, via the Patterson function, is used by X-ray diffractionists to help recover the "Fourier phase information" on atom positions not available through diffraction alone.
• In statistics, spatial autocorrelation between sample locations also helps one estimate
  mean value uncertainties
  when sampling a heterogeneous population.
• The
  SEQUEST algorithm for analyzing mass spectra makes use of autocorrelation in conjunction with cross-correlation to score the similarity of an observed spectrum to an idealized spectrum representing a peptide
  .
• In astrophysics, autocorrelation is used to study and characterize the spatial distribution of galaxies in the universe and in multi-wavelength observations of low mass X-ray binaries.
• In panel data, spatial autocorrelation refers to correlation of a variable with itself through space.
• In analysis of Markov chain Monte Carlo data, autocorrelation must be taken into account for correct error determination.
• In
  geosciences (specifically in geophysics
  ) it can be used to compute an autocorrelation seismic attribute, out of a 3D seismic survey of the underground.
• In medical ultrasound imaging, autocorrelation is used to visualize blood flow.
• In intertemporal portfolio choice, the presence or absence of autocorrelation in an asset's rate of return can affect the optimal portion of the portfolio to hold in that asset.
• In numerical relays, autocorrelation has been used to accurately measure power system frequency.^[19]

Serial dependence

Serial dependence is closely linked to the notion of autocorrelation, but represents a distinct concept (see

). In particular, it is possible to have serial dependence but no (linear) correlation. In some fields however, the two terms are used as synonyms.

A time series of a random variable has serial dependence if the value at some time ${\displaystyle t}$ in the series is

on the value at another time ${\displaystyle s}$. A series is serially independent if there is no dependence between any pair.

If a time series ${\displaystyle \left\{X_{t}\right\}}$ is stationary, then statistical dependence between the pair ${\displaystyle (X_{t},X_{s})}$ would imply that there is statistical dependence between all pairs of values at the same lag ${\displaystyle \tau =s-t}$.

See also

References

Further reading

• ISBN 978-0-02-365070-3
  .
• ISBN 978-1-119-40110-0
  .
• Mojtaba Soltanalian, and Petre Stoica. "Computational design of sequences with good correlation properties." IEEE Transactions on Signal Processing, 60.5 (2012): 2180–2193.
• Solomon W. Golomb, and Guang Gong. Signal design for good correlation: for wireless communication, cryptography, and radar. Cambridge University Press, 2005.
• Klapetek, Petr (2018). Quantitative Data Processing in Scanning Probe Microscopy: SPM Applications for Nanometrology (Second ed.). Elsevier. pp. 108–112
  ISBN 9780128133477
  .
• Weisstein, Eric W. "Autocorrelation". MathWorld.