Keresztösszefüggés

Keresztösszefüggés is a Hungarian term that translates to cross-correlation in English. It refers to a measure of similarity between two series of signals as a function of the displacement of one relative to the other. In simpler terms, it helps to understand how two different datasets or signals relate to each other when one is shifted or delayed.

The concept is widely used in various scientific and engineering fields. In signal processing, it is employed to detect a known signal within another signal, such as identifying a specific pattern in audio or video data. It can also be used for time-delay estimation, determining the time difference between two recordings of the same event, for example, in sonar or radar systems.

In statistics and econometrics, cross-correlation is used to analyze the relationship between two time series. It helps identify if and when changes in one series tend to precede or follow changes in another. For instance, it might be used to examine the relationship between advertising expenditure and sales, or between interest rates and inflation.

Mathematically, cross-correlation involves multiplying one signal by a time-shifted version of another signal and integrating the result over time. The output is a function that indicates the degree of similarity at each possible shift. A high value typically signifies a strong similarity at that particular displacement.