Hi Muslih AI, 

Pearson's R is a measure of Linear correlation between two variables namely X and Y. Where as cross correlation is the amplitude and lag difference between two wave patterns or forms. It is mostly used for repetitive phenomena, such as "Signaling" or "Stock Markets".

Regards,

Thanks dear Mohamed

Pearson correlation coefficient (r)  show the linear correlation between them. If  r value is high (>0.8) then you may use linear regression that give better result. 

cross-correlation is similar to pearson correlation coefficient but it is used for measuring  similarity of two series as a function of the lag of one relative to the other.

Thank you dear Lov

Correlation is relation which is a set of order pairs of observations and tells how two variables vary together.  Regression is used for prediction and it is imperative to know which variable is the dependent "Y" and which is the independent "X". Furthermore, you have to have assumptions underlying the error term in regression  and you are interested in goodness of fit.. 

Thank you Dr Abdulrazzak

for more than two time series, regression can be used the resulted correlation coefficient (Adjusted R Square) take into consideration the added independent variable the more the R value shows the better relationship

this can be done by excel tool

Best regards to the all