Autocorrelation


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Related to Autocorrelation: multicollinearity, Autocorrelation function, Cross correlation

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 ?
This approach is based on the computation of the generalized Hough transform with the candidate peaks ordered according to their region of dominance in the autocorrelation function.
This can generate spurious outliers, and weaken the reliability of some tests of spatial autocorrelation.
This assumption may not be valid with variables representing a geographically varying phenomenon, such as population, because when the spatial scale moves down especially to the block level, the spatial autocorrelation of population among neighboring blocks is likely to become stronger (Brown 1995).
Following our earlier results (Kahya and Marti, 2007) for this analysis phase, we here prefer to present the results of autocorrelation calculations in a tabular fashion (Table 1).
00 suggesting that serial or autocorrelation was not a problem in the data set.
I used genealogical and genetic spatial autocorrelation analyses of raccoons sampled over 5-9 years on six trapping grids in southwestern Tennessee to assess these predictions.
b) In some cases, data conversion into different components makes the autocorrelation.
A stationarity requirement, defined as a constant mean, variance and autocorrelation through time, constrains parameters to a certain range.
In this case, spatial econometrics is needed to recognise the existence of spatial autocorrelation.
Autocorrelation was computed to test the regularity [14] of the vibration periods on the time-series of the accelerometer-enveloped signal recorded for each vibrator over the minute of each trial.
In particular, the autocorrelation function (ACF) and partial autocorrelation function (PACF) can point to recurrent "lags" in the data, so that peaks may always occur, on average, every quarter, six months, or twelve months (Farnum and Stanton 1989a, 1989d).
Their methodology had the advantage of controlling for other variables that could affect demand, and using ARIMA modeling to eliminate problematic autocorrelation.