The half-fractionation method is the best method of stability, which is divided into two parts, which is divided into two equal parts. The reason for division is to reduce the factors affecting the performance of individuals of the sample such as time, effort, fatigue, boredom, etc.

The half-fractionation method is the best method of stability, which is divided into two parts, which is divided into two equal parts. The reason for division is to reduce the factors affecting the performance of individuals of the sample such as time, effort, fatigue, boredom, etc.

Dear Omar Hasnaoui

The best way to do that in R is using the ccf (cross-correlation) function. This function allows you to determine the lag at which the correlation between two time series is strongest. Two important things that you must ensure when you run a cross-correlation: your time series is stationary.

To understand the LAG:

Consider a discrete sequence of values, for lag 1, you compare your time series with a lagged time series. It means you shift the time series by 1 before comparing it with itself. Proceed doing this for the entire length of time series by shifting it by 1 every time.

Please see the following article which might help you understand your issue:

https://towardsdatascience.com/cross-correlation-of-currency-pairs-in-r-ccf-d27eec2d4b91?gi=80d0f15c32f

Good luck

Check Stationarity and how to test in this section:

https://otexts.com/fpp2/stationarity.html