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Example Sentences for "autocorrelation "

Although more sophisticated analyses such as autocorrelation and MESA (described below) are required to evaluate rhythmic signals quantitatively, it is often possible to make meaningful qualitative assessments by inspecting a plot of raw data.

We will return to the other panels in Figure 2when we discuss the quantitative analysis of periodicity using autocorrelation and MESA.

Rhythmic variation in this autocorrelation function uncovers periodicity.

Note that as the lag between the data series increases, the number of nonoverlapping points increases and the autocorrelation analysis involves a diminishing portion of the signal.

A reasonable question is whether or not the periodicity signified by the outcome of an autocorrelation treatment can occur by chance alone.

It is possible to assess quantitatively how likely a given peak in an autocorrelation can be the result of chance alone.

There is precedence for a less quantitative assessment of rhythmicity using the autocorrelation function [ 37 38 ] .

Even when the autocorrelation function portrays statistically significant rhythmicity, it is still possible that the data do not represent a truly rhythmic process.

Given that the autocorrelation function is a good measure of the amplitude across the entire span of the signal, and that the rate of "decay" in this function reliably assesses the long-range regularity in the data [ 37 ] we employ an index derived from this function as a measure of how rhythmic the data are.

There are other practical considerations: in the case of circadian data, especially when only 4-5 days of data are available, the 3 rdpeak incorporates only half the original data (as the autocorrelation analysis is calculated each successive lag produces a loss of a data point for subsequent consideration, thus after one day 24 one hour points would be out of the analysis).

In the first general class of techniques, one takes the transform of either the autocovariance function or, more usually, the autocorrelation function [ 47 ] .

When computing the autocorrelation function, data are lost at either end with each advancing lag, so computation values seldom proceeds past the point when about 1/3 of the original data set has been lost.

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