A plot showing 100 random numbers with a "hidden" sine function, and an autocorrelation (correlogram) of the series on the bottom.
In the analysis of data, a correlogram is a chart of correlation statistics.
For example, in time series analysis, a plot of the sample autocorrelations versus (the time lags) is an autocorrelogram.
If cross-correlation is plotted, the result is called a cross-correlogram.
The correlogram is a commonly used tool for checking randomness in a data set. If random, autocorrelations should be near zero for any and all time-lag separations. If non-random, then one or more of the autocorrelations will be significantly non-zero.
In addition, correlograms are used in the model identification stage for Box–Jenkins autoregressive moving average time series models. Autocorrelations should be near-zero for randomness; if the analyst does not check for randomness, then the validity of many of the statistical conclusions becomes suspect. The correlogram is an excellent way of checking for such randomness.
In multivariate analysis, correlation matrices shown as color-mapped images may also be called "correlograms" or "corrgrams".[1][2][3]
^Friendly, Michael (19 August 2002). "Corrgrams: Exploratory displays for correlation matrices" (PDF). The American Statistician. 56 (4). Taylor & Francis: 316–324. doi:10.1198/000313002533. Retrieved 19 January 2014.
^"CRAN – Package corrgram". cran.r-project.org. 29 August 2013. Retrieved 19 January 2014.
^"Quick-R: Correlograms". statmethods.net. Retrieved 19 January 2014.
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