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In the statistics section, the definition of autocorrelation is not the same as those often given in many books on stochastic processes, and it differs from the definition given in many places on Wikipedia itself. It is a valid notation, however. For example, this notation is used in the following two books and any other books/notes on "time-series analysis".
On the other hand, I have found a definition of autocorrelation to simply be for two times and in these:
In other words, the alternative definition for autocorrelation deserves to be expressed with some formality in the context of stochastic processes (i.e. ) in the "statistics" section also. And it should be dealt with explicitly that these two functions have the same name in differing contexts (seems to be "time-series analysis" and "everywhere else"). This has several advantages of at least admitting this alternative definition exists not merely in the context of "signal processing" with its integral definition (rather than the probabilistic one with the expectation):
— Preceding unsigned comment added by 71.80.79.67 ( talk) 06:59, 4 February 2014 (UTC)
This passage:
"For a weak-sense stationarity, wide-sense stationarity (WSS) process, the definition is
where
"
seems unclear to me, because to the uninitiated (like me), the phrase:
"For a weak-sense stationarity, wide-sense stationarity (WSS) process"
seems to be self-contradictory nonsense.
I hope someone knowledgeable about this subject will please rewrite this so that it does not come across as nonsense.
Please note: I am not saying that it is nonsense. But since it appears to be nonsense, this would benefit from some clarification.
Is there supposed to be an and between "weak-sense stationarity" and "wide-sense stationarity" ??? An or ??? Or what??? 2601:200:C000:1A0:300E:BD77:DEE5:AA45 ( talk) 03:27, 17 August 2022 (UTC)
If it's obvious vadalism
DavidMCEddy ( talk) 06:44, 17 August 2022 (UTC)
The section Auto-correlation of stochastic processes begins as follows:
"In statistics, the autocorrelation of a real or complex random process is the Pearson correlation between values of the process at different times, as a function of the two times or of the time lag."
The definition is then presented as
(Eq.1) |
(where is the expected value operator and the bar represents complex conjugation.)
However, this does not use Pearson correlation, but instead uses covariance.
Later in the article it is explained that there are two conventions, one for using Pearson correlation and another for using covriance (and calllling it "correlation" anyway).
Very bad idea. The passage I quote above contradicts itself.
And regardless of whether some people misuse the word "correlation" to mean "covariance", that should not be what Wikipedia does. 2601:200:C000:1A0:1841:4827:BAAA:F4DF ( talk) 15:52, 17 August 2022 (UTC)
This is the
talk page for discussing improvements to the
Autocorrelation article. This is not a forum for general discussion of the article's subject. |
Article policies
|
Find sources: Google ( books · news · scholar · free images · WP refs) · FENS · JSTOR · TWL |
Archives: 1Auto-archiving period: 90 days |
This article is rated C-class on Wikipedia's
content assessment scale. It is of interest to the following WikiProjects: | |||||||||||
|
In the statistics section, the definition of autocorrelation is not the same as those often given in many books on stochastic processes, and it differs from the definition given in many places on Wikipedia itself. It is a valid notation, however. For example, this notation is used in the following two books and any other books/notes on "time-series analysis".
On the other hand, I have found a definition of autocorrelation to simply be for two times and in these:
In other words, the alternative definition for autocorrelation deserves to be expressed with some formality in the context of stochastic processes (i.e. ) in the "statistics" section also. And it should be dealt with explicitly that these two functions have the same name in differing contexts (seems to be "time-series analysis" and "everywhere else"). This has several advantages of at least admitting this alternative definition exists not merely in the context of "signal processing" with its integral definition (rather than the probabilistic one with the expectation):
— Preceding unsigned comment added by 71.80.79.67 ( talk) 06:59, 4 February 2014 (UTC)
This passage:
"For a weak-sense stationarity, wide-sense stationarity (WSS) process, the definition is
where
"
seems unclear to me, because to the uninitiated (like me), the phrase:
"For a weak-sense stationarity, wide-sense stationarity (WSS) process"
seems to be self-contradictory nonsense.
I hope someone knowledgeable about this subject will please rewrite this so that it does not come across as nonsense.
Please note: I am not saying that it is nonsense. But since it appears to be nonsense, this would benefit from some clarification.
Is there supposed to be an and between "weak-sense stationarity" and "wide-sense stationarity" ??? An or ??? Or what??? 2601:200:C000:1A0:300E:BD77:DEE5:AA45 ( talk) 03:27, 17 August 2022 (UTC)
If it's obvious vadalism
DavidMCEddy ( talk) 06:44, 17 August 2022 (UTC)
The section Auto-correlation of stochastic processes begins as follows:
"In statistics, the autocorrelation of a real or complex random process is the Pearson correlation between values of the process at different times, as a function of the two times or of the time lag."
The definition is then presented as
(Eq.1) |
(where is the expected value operator and the bar represents complex conjugation.)
However, this does not use Pearson correlation, but instead uses covariance.
Later in the article it is explained that there are two conventions, one for using Pearson correlation and another for using covriance (and calllling it "correlation" anyway).
Very bad idea. The passage I quote above contradicts itself.
And regardless of whether some people misuse the word "correlation" to mean "covariance", that should not be what Wikipedia does. 2601:200:C000:1A0:1841:4827:BAAA:F4DF ( talk) 15:52, 17 August 2022 (UTC)