autocorrelation

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autocorrelation

[¦ȯd·ō‚kär·ə′lā·shən]
(electronics)
A technique used to detect cyclic activity in a complex signal.
(statistics)
In a time series, the relationship between values of a variable taken at certain times in the series and values of a variable taken at other, usually earlier times.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
It is clear that the absolute values of ACFs decrease as the time separation increases whether in the 3D or 2D model which implies that auto-correlation values drop.
Analysis of the temporal auto-correlation of the provided feed types, as well as the total dry mass of provided feed was done with a maximum time-lag of 10 years (Clark & Bjornstad 2004).
To test the auto-correlation, Durbin-Watson (D.W) test was applied, according to which if the estimated value falls between dL=1.73 and dU=2.3 then there will be no auto-correlation in the model.
The Durbin-Watson d = 1.831, which is between the two critical values of 1.5 < d < 2.5 and therefore we can assume that there is no first order linear auto-correlation in our multiple linear regression data.
12 shows that in this regime a strong auto-correlation prevails until the bit 600 approximately.
Therefore, it is necessary to select the correct statistical amount to analyze the spatial auto-correlation according to the different types of the spatial data's observed values.
Given this lack of support for between-segment dependence, models accounting for spatial auto-correlation were not considered further.
Poterba and Summers (1988) suggested that financial markets show a positive auto-correlation in the short-term and a negative autocorrelation in the long-term.
By using the periodic statistics such as mean and auto-correlation of the primary waveform, CR can detect a random signal with a specific modulation type in the presence of random stochastic noise.
Durbin-Watson statistic has been used to analyse the problem of auto-correlation. The result (1.837) indicates that variables are not auto correlated as the value is closer to 2.
Appropriate ARIMA models were fitted after judging the time-series data for stationarity based on visual inspection, auto-correlation function and partial auto-correlation function.