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Autocorrelation Definition from Business & Finance Dictionaries & Glossaries
The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation.
Copyright © 2000, Campbell R. Harvey. All Rights Reserved.
[GIS] 1. Spatial dependence
2. Interdependence(correlation) of errors at different points in space;
3. Statistical correlation between spatial random variables of the same type, attribute, name, and so on, where the correlation depends on the distance and/or direction that separates the locations.
Compare to crosscorrelation.
Note: strong negative correlation will lead to large errors in distance; independence of errors leading to moderate errors in distance; strong positive correlation leading to no errors in distance.
eg: If an entire field is planted with a single crop, such as barley, then only one point needs to be observed to determine the crop of the entire field.
2. Interdependence(correlation) of errors at different points in space;
3. Statistical correlation between spatial random variables of the same type, attribute, name, and so on, where the correlation depends on the distance and/or direction that separates the locations.
Compare to crosscorrelation.
Note: strong negative correlation will lead to large errors in distance; independence of errors leading to moderate errors in distance; strong positive correlation leading to no errors in distance.
eg: If an entire field is planted with a single crop, such as barley, then only one point needs to be observed to determine the crop of the entire field.
the problem of interdependence among successive values of the disturbance term. The problem with autocorrelation
Copyright © 2001, Ray WrightAutocorrelation Definition from Encyclopedia Dictionaries & Glossaries
Autocorrelation is the cross-correlation of a signal with itself. Informally, it is the similarity between observations as a function of the time separation between them. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal which has been buried under noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.
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