Autocorrelation

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Autocorrelation

The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation.

Serial Correlation

In technical analysis, a measure of how well past occurrences predict future occurrences. Most importantly, serial correlation checks whether and how often a particular price movement will result in a different price movement. Serial correlation lies at the heart of technical analysis. It is also called autocorrelation.
References in periodicals archive ?
With regard to goodness of fit, residuals were randomly distributed with no autocorrelation among them.
The seasonal component can be examined via autocorrelation correlograms, which display serial correlation for consecutive lags.
589 Table 3 Autocorrelations and Q Statistics of Returns Series 1 2 3 AEX, AC -0.
2]O emissions and soil temperature, with adjustment for season and autocorrelation at a lag of 1 week.
Spatial autocorrelation occurs when the spatial distribution of the variable of interest exhibits a systematic pattern [Cliff and Ord (1981)].
Presidential Dummy and Residual Autocorrelations This table reports the autocorrelations of the portfolios excess returns [r.
By what has been done in Section 2, in the setting of Z, this implies that bounded real valued sequences can be constructed whose autocorrelations are inverse Fourier transforms of linear combinations of such functions, see Example 3.
The aperiodic autocorrelation function (ACF) of sequence S of length N is given as,
LMVRT Natural logarithm of the weekly relative change in the MVI IXXI Binary Indicator 0 - Pre 9/11/01 1 - Post 9/11/01 Table 9: Autocorrelations of LnSP500RT (* denotes significant values) LAG ACF SE PACF SE 1 -0.
The traditional method consists in comparing the observed patterns of the sample autocorrelation and partial autocorrelation functions with the theoretical ACF and PACF patterns.
ACF Table 1: Model Description Model Name MOD_1 Series Name 1 Current Year Dividend 2 Profit for Current Year 3 Dividend of Previous Year Transformation None Non-Seasonal Differencing 0 Seasonal Differencing 0 Length of Seasonal Period No periodicity Maximum Number of Lags 16 Process Assumed for Independence(white noise)(a) Calculating the Standard Errors of the Autocorrelations Display and Plot All lags Applying the model specifications from MOD_1 a Not applicable for calculating the standard errors of the partial autocorrelations.
The exercises are very helpful and Madsen also provides extra material on partial autocorrelations and some results from trigonometry.