Translation and analysis of words by ChatGPT artificial intelligence
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linear autocorrelation - translation to russian
![Correlogram example from 400-point sample of a first-order autoregressive process with 0.75 correlation of adjacent points, along with the 95% [[confidence interval]]s (plotted about the correlation estimates in black and about zero in red), as calculated by the equations in this section. The dashed blue line shows the actual autocorrelation function of the sampled process.](https://commons.wikimedia.org/wiki/Special:FilePath/Correlogram example.png?width=200 "Correlogram example from 400-point sample of a first-order autoregressive process with 0.75 correlation of adjacent points, along with the 95% [[confidence interval]]s (plotted about the correlation estimates in black and about zero in red), as calculated by the equations in this section. The dashed blue line shows the actual autocorrelation function of the sampled process.")
математика
линейная автокорреляция
![Above: A plot of a series of 100 random numbers concealing a [[sine]] function. Below: The sine function revealed in a [[correlogram]] produced by autocorrelation.](https://commons.wikimedia.org/wiki/Special:FilePath/Acf new.svg?width=200 "Above: A plot of a series of 100 random numbers concealing a [[sine]] function. Below: The sine function revealed in a [[correlogram]] produced by autocorrelation.")
Автокорреляция
[ɔ:tə(u)kɔri'leiʃ(ə)n]
общая лексика
автокорреляция
сериальная корреляция
существительное
математика
автокорреляция
Definition
Wikipedia
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".
