CorrelationFunction

CorrelationFunction[data, hspec]
estimates the correlation function at lags hspec from data.

CorrelationFunction[proc, hspec]
represents the correlation function at lags hspec for the random process proc.

CorrelationFunction[proc, s, t]
represents the correlation function at times s and t for the random process proc.

DetailsDetails

  • CorrelationFunction is also known as autocorrelation or cross-correlation function (ACF or CCF).
  • CorrelationFunction of the process proc is the CovarianceFunction c normalized by the outer product of the standard deviation function at times s and t:
  • c[s,t]/([s][t])for scalar-valued data or processes
    c[s,t]/([s] [t])for vector-valued data or processes
  • CorrelationFunction[proc, h] is defined only if proc is a weakly stationary process and is equivalent to CorrelationFunction[proc, 0, h].
  • The process proc can be any random process such as ARMAProcess or WienerProcess.
  • CorrelationFunction[{x1, ..., xn}, h] is equivalent to with =Mean[{x1, ..., xn}].
  • When data is TemporalData containing an ensemble of paths, the output represents the average across all paths.
  • The following specifications can be given for hspec:
  • at time or lag
    {max}unit spaced from 0 to
    {min,max}unit spaced from to
    {min,max,d}from to in steps of d
    {{1,2,...}}use explicit

ExamplesExamplesopen allclose all

Basic Examples (4)Basic Examples (4)

Estimate the correlation function at lag 2:

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The sample correlation function for a random sample from an autoregressive time series:

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The correlation function for a discrete-time process:

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The correlation function for a continuous-time process:

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