# Autocorrelation

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Autocorrelation, sometimes known as serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations as a function of the time lag between them. 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. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.

## Contents

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.

Unit root processes, trend-stationary processes, autoregressive processes, and moving average processes are specific forms of processes with autocorrelation.

## Auto-correlation of stochastic processes

In statistics, the autocorrelation of a real or complex random process is the Pearson correlation 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 integer for a discrete-time process or a real number for a 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

${\displaystyle \operatorname {R} _{XX}(t_{1},t_{2})=\operatorname {E} \left[X_{t_{1}}{\overline {X}}_{t_{2}}\right]}$

(Eq.1)

where ${\displaystyle \operatorname {E} }$ is the expected value operator and the bar represents complex conjugation. Note that the expectation may not be well defined.

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

${\displaystyle \operatorname {K} _{XX}(t_{1},t_{2})=\operatorname {E} \left[(X_{t_{1}}-\mu _{t_{1}}){\overline {(X_{t_{2}}-\mu _{t_{2}})}}\right]=\operatorname {E} \left[X_{t_{1}}{\overline {X}}_{t_{2}}\right]-\mu _{t_{1}}{\overline {\mu }}_{t_{2}}}$

(Eq.2)

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 wide-sense stationary process 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 even function of the lag ${\displaystyle \tau =t_{2}-t_{1}}$. This gives the more familiar forms for the auto-correlation function [1] :p.395

${\displaystyle \operatorname {R} _{XX}(\tau )=\operatorname {E} \left[X_{t+\tau }{\overline {X}}_{t}\right]}$

(Eq.3)

and the auto-covariance function:

${\displaystyle \operatorname {K} _{XX}(\tau )=\operatorname {E} \left[(X_{t+\tau }-\mu ){\overline {(X_{t}-\mu )}}\right]=\operatorname {E} \left[X_{t+\tau }{\overline {X}}_{t}\right]-\mu {\overline {\mu }}}$

(Eq.4)

### Normalization

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

${\displaystyle \rho _{XX}(t_{1},t_{2})={\frac {\operatorname {K} _{XX}(t_{1},t_{2})}{\sigma _{t_{1}}\sigma _{t_{2}}}}={\frac {\operatorname {E} \left[(X_{t_{1}}-\mu _{t_{1}}){\overline {(X_{t_{2}}-\mu _{t_{2}})}}\right]}{\sigma _{t_{1}}\sigma _{t_{2}}}}.}$

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 anti-correlation.

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

${\displaystyle \rho _{XX}(\tau )={\frac {\operatorname {K} _{XX}(\tau )}{\sigma ^{2}}}={\frac {\operatorname {E} \left[(X_{t+\tau }-\mu ){\overline {(X_{t}-\mu )}}\right]}{\sigma ^{2}}}}$

where

${\displaystyle \operatorname {K} _{XX}(0)=\sigma ^{2}.}$

The normalization is important both because the interpretation of the autocorrelation as a correlation provides a scale-free measure of the strength of statistical dependence, 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 even function can be stated as [2] :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

${\displaystyle \operatorname {R} _{XX}(\tau )={\overline {\operatorname {R} _{XX}(-\tau )}}.}$

#### Maximum at zero

For a WSS process: [2] :p.174

${\displaystyle \left|\operatorname {R} _{XX}(\tau )\right|\leq \operatorname {R} _{XX}(0)}$

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

#### Cauchy–Schwarz inequality

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

${\displaystyle \left|\operatorname {R} _{XX}(t_{1},t_{2})\right|^{2}\leq \operatorname {E} \left[|X_{t_{1}}|^{2}\right]\operatorname {E} \left[|X_{t_{2}}|^{2}\right]}$

#### 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:

${\displaystyle \operatorname {R} _{XX}(\tau )=\int _{-\infty }^{\infty }S_{XX}(f)e^{i2\pi f\tau }\,{\rm {d}}f}$
${\displaystyle S_{XX}(f)=\int _{-\infty }^{\infty }\operatorname {R} _{XX}(\tau )e^{-i2\pi f\tau }\,{\rm {d}}\tau .}$

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:

${\displaystyle \operatorname {R} _{XX}(\tau )=\int _{-\infty }^{\infty }S_{XX}(f)\cos(2\pi f\tau )\,{\rm {d}}f}$
${\displaystyle S_{XX}(f)=\int _{-\infty }^{\infty }\operatorname {R} _{XX}(\tau )\cos(2\pi f\tau )\,{\rm {d}}\tau .}$

## Auto-correlation of random vectors

The (potentially time-dependent) auto-correlation matrix (also called second moment) of a (potentially time-dependent) random vector ${\displaystyle \mathbf {X} =(X_{1},\ldots ,X_{n})^{\rm {T}}}$ 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 random vector ${\displaystyle \mathbf {X} =(X_{1},\ldots ,X_{n})^{\rm {T}}}$ containing random elements whose expected value and variance exist, the auto-correlation matrix is defined by [3] :p.190 [1] :p.334

${\displaystyle \operatorname {R} _{\mathbf {X} \mathbf {X} }\triangleq \ \operatorname {E} \left[\mathbf {X} \mathbf {X} ^{\rm {T}}\right]}$

(Eq.5)

where ${\displaystyle {}^{\rm {T}}}$ denotes transposition and has dimensions ${\displaystyle n\times n}$.

Written component-wise:

${\displaystyle \operatorname {R} _{\mathbf {X} \mathbf {X} }={\begin{bmatrix}\operatorname {E} [X_{1}X_{1}]&\operatorname {E} [X_{1}X_{2}]&\cdots &\operatorname {E} [X_{1}X_{n}]\\\\\operatorname {E} [X_{2}X_{1}]&\operatorname {E} [X_{2}X_{2}]&\cdots &\operatorname {E} [X_{2}X_{n}]\\\\\vdots &\vdots &\ddots &\vdots \\\\\operatorname {E} [X_{n}X_{1}]&\operatorname {E} [X_{n}X_{2}]&\cdots &\operatorname {E} [X_{n}X_{n}]\\\\\end{bmatrix}}}$

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

${\displaystyle \operatorname {R} _{\mathbf {Z} \mathbf {Z} }\triangleq \ \operatorname {E} [\mathbf {Z} \mathbf {Z} ^{\rm {H}}].}$

Here ${\displaystyle {}^{\rm {H}}}$ denotes Hermitian transposition.

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 signal ${\displaystyle f(t)}$, 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

${\displaystyle R_{ff}(\tau )=\int _{-\infty }^{\infty }f(t+\tau ){\overline {f(t)}}\,{\rm {d}}t=\int _{-\infty }^{\infty }f(t){\overline {f(t-\tau )}}\,{\rm {d}}t}$

(Eq.6)

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

${\displaystyle R_{yy}(\ell )=\sum _{n\in Z}y(n)\,{\overline {y(n-\ell )}}}$

(Eq.7)

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 wide-sense-stationary random processes, the autocorrelations are defined as

{\displaystyle {\begin{aligned}R_{ff}(\tau )&=\operatorname {E} \left[f(t){\overline {f(t-\tau )}}\right]\\R_{yy}(\ell )&=\operatorname {E} \left[y(n)\,{\overline {y(n-\ell )}}\right].\end{aligned}}}

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]

{\displaystyle {\begin{aligned}R_{ff}(\tau )&=\lim _{T\rightarrow \infty }{\frac {1}{T}}\int _{0}^{T}f(t+\tau ){\overline {f(t)}}\,{\rm {d}}t\\R_{yy}(\ell )&=\lim _{N\rightarrow \infty }{\frac {1}{N}}\sum _{n=0}^{N-1}y(n)\,{\overline {y(n-\ell )}}.\end{aligned}}}

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 ${\displaystyle T}$:

${\displaystyle R_{ff}(\tau )\triangleq \int _{t_{0}}^{t_{0}+T}f(t+\tau ){\overline {f(t)}}\,dt}$

which is equivalent to

${\displaystyle R_{ff}(\tau )\triangleq \int _{t_{0}}^{t_{0}+T}f(t){\overline {f(t-\tau )}}\,dt}$

### 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-dimensional autocorrelation is defined similarly. For example, in three dimensions the autocorrelation of a square-summable discrete signal would be

${\displaystyle R(j,k,\ell )=\sum _{n,q,r}x_{n,q,r}\,{\overline {x}}_{n-j,q-k,r-\ell }.}$

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 discrete sequence, it is frequently necessary to compute the autocorrelation with high computational efficiency. A brute force method 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:

${\displaystyle {\begin{array}{rrrrrr}&2&3&-1\\\times &2&3&-1\\\hline &-2&-3&1\\&&6&9&-3\\+&&&4&6&-2\\\hline &-2&3&14&3&-2\end{array}}}$

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 ]

{\displaystyle {\begin{aligned}F_{R}(f)&=\operatorname {FFT} [X(t)]\\S(f)&=F_{R}(f)F_{R}^{*}(f)\\R(\tau )&=\operatorname {IFFT} [S(f)]\end{aligned}}}

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 discrete 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

${\displaystyle {\hat {R}}(k)={\frac {1}{(n-k)\sigma ^{2}}}\sum _{t=1}^{n-k}(X_{t}-\mu )(X_{t+k}-\mu )}$

for any positive integer ${\displaystyle k. When the true mean ${\displaystyle \mu }$ and variance ${\displaystyle \sigma ^{2}}$ are known, this estimate is unbiased . If the true mean and variance 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 regression analysis using time series data, autocorrelation in a variable of interest is typically modeled either with an autoregressive model (AR), a moving average model (MA), their combination as an autoregressive-moving-average model (ARMA), or an extension of the latter called an autoregressive integrated moving average model (ARIMA). With multiple interrelated data series, vector autoregression (VAR) or its extensions are used.

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. Problematic autocorrelation of the errors, which themselves are unobserved, can generally be detected because it produces autocorrelation in the observable residuals. (Errors are also known as "error terms" in econometrics.) Autocorrelation of the errors violates the ordinary least squares assumption that the error terms are uncorrelated, meaning that the Gauss Markov theorem does not apply, and that OLS estimators are no longer the Best Linear Unbiased Estimators (BLUE). While it does not bias the OLS coefficient estimates, the standard errors tend to be underestimated (and the 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 generalized least squares and the Newey–West HAC estimator (Heteroskedasticity and Autocorrelation Consistent). [13]

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 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 dynamic light scattering data, which notably enables determination of the particle size distributions of nanometer-sized particles or micelles suspended in a fluid. A laser shining into the mixture produces a 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 GPS system to correct for the propagation delay, or time shift, between the point of time at the transmission of the carrier signal at the satellites, and the point of time at the receiver on the ground. This is done by the receiver generating a replica signal of the 1,023-bit C/A (Coarse/Acquisition) code, and generating lines of code chips [-1,1] in packets of ten at a time, or 10,230 chips (1,023 × 10), shifting slightly as it goes along in order to accommodate for 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 signal processing, autocorrelation can give information about repeating events like musical beats (for example, to determine tempo) or pulsar frequencies, though it cannot tell the position in time of the beat. It can also be used to estimate the pitch of a musical tone.
• In music recording, autocorrelation is used as a pitch detection algorithm prior to vocal processing, as a distortion effect or to eliminate undesired mistakes and inaccuracies. [18]
• 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.

## Serial dependence

Serial dependence is closely linked to the notion of autocorrelation, but represents a distinct concept (see Correlation and dependence). 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 statistically dependent 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 ${\displaystyle \tau =s-t}$.

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In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions. 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.

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

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.

Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used in complex statistical models consisting of observed variables as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as might be described by a graphical model. As typical in Bayesian inference, the parameters and latent variables are grouped together as "unobserved variables". Variational Bayesian methods are primarily used for two purposes:

1. To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables.
2. To derive a lower bound for the marginal likelihood of the observed data. This is typically used for performing model selection, the general idea being that a higher marginal likelihood for a given model indicates a better fit of the data by that model and hence a greater probability that the model in question was the one that generated the data.

In statistics, econometrics and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it is used to describe certain time-varying processes in nature, economics, etc. The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term ; thus the model is in the form of a stochastic difference equation. Together with the moving-average (MA) model, it is a special case and key component of the more general autoregressive–moving-average (ARMA) and autoregressive integrated moving average (ARIMA) models of time series, which have a more complicated stochastic structure; it is also a special case of the vector autoregressive model (VAR), which consists of a system of more than one interlocking stochastic difference equation in more than one evolving random variable.

Directional statistics is the subdiscipline of statistics that deals with directions, axes or rotations in Rn. More generally, directional statistics deals with observations on compact Riemannian manifolds including the Stiefel manifold.

In probability and statistics, given two stochastic processes and , the cross-covariance is a function that gives the covariance of one process with the other at pairs of time points. With the usual notation for the expectation operator, if the processes have the mean functions and , then the cross-covariance is given by

A cyclostationary process is a signal having statistical properties that vary cyclically with time. A cyclostationary process can be viewed as multiple interleaved stationary processes. For example, the maximum daily temperature in New York City can be modeled as a cyclostationary process: the maximum temperature on July 21 is statistically different from the temperature on December 20; however, it is a reasonable approximation that the temperature on December 20 of different years has identical statistics. Thus, we can view the random process composed of daily maximum temperatures as 365 interleaved stationary processes, each of which takes on a new value once per year.

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.

In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals from a regression analysis. It is named after James Durbin and Geoffrey Watson. The small sample distribution of this ratio was derived by John von Neumann. Durbin and Watson applied this statistic to the residuals from least squares regressions, and developed bounds tests for the null hypothesis that the errors are serially uncorrelated against the alternative that they follow a first order autoregressive process. Note that the distribution of this test statistic does not depend on the estimated regression coefficients and the variance of the errors.

In statistical signal processing, the goal of spectral density estimation (SDE) is to estimate the spectral density of a random signal from a sequence of time samples of the signal. Intuitively speaking, the spectral density characterizes the frequency content of the signal. One purpose of estimating the spectral density is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities.

In probability theory, the family of complex normal distributions, denoted or , characterizes complex random variables whose real and imaginary parts are jointly normal. The complex normal family has three parameters: location parameter μ, covariance matrix , and the relation matrix . The standard complex normal is the univariate distribution with , , and .

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