We can study the phenomena that the value of the step is lead from previous steps by self correlation analysis.
Usually, Correlation is an approach to study the relationship between different variables (X and Y). But self correlation is an approach to study single variable.
In self correlation, X is a previous value. So same value could be both X and Y.
Generally, we use various information to predict something.
But if self correlation is strong, we can predict using only single variable.
This approach seems to be something special method. We use this method for daily use. For example, it is that "Today's afternoon will be sunny because the sunny days are kept 3 days recently."
I use "X(n)", not "X(t)."
It is a time series analysis, but I use "n."
"n" means "the number is n" or "n th."
If the value is measured by 1 hour,
"Number n = n hours"
. But it is a special case.
In time series analysis, Meta knowledge of sampling is important. There are two cases. One of them is that the sampling is done by same intervals, "every 1 second", "every 1 hour." The other is that intervals are different, "every lot", "every accident."
For both case, software deals with in a same way. But we need to take care of the sampling when we understand the output.
In self correlation analysis, the phenomena between samplings are ignored. In other words, self correlation analysis does not study continuous phenomena.
This is not so important if the change between samplings is not important. But we need to take care to understand the output when we do not know the information of sampling.
The change between steps could be studied as "mapping" of the logic of mathematics because discrete data is used.
This approach is often used to study chaos.
The relationship between Single Regression Analysis and Multi-Regression Analysis is similar to that of Single Self Correlation Analysis and AR Model.
Single Regression Analysis is used in many stages of analysis because it is a simple tool. In a similar way, the use of Single Self Correlation Analysis has variation.
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