Comparison of two stochastic processes

Let the two tables OBSERVA.tbl and OBSERVB.tbl contain two sets of observations. Each set is stored in the DOUBLE PRECISION columns :TIME, :VALUE and :VAR containing the times of observation, data value and their variances.

CREATE/GRAPHICS 		 ! Create graphics window
SET/CONTEXT TSA 		 ! Enable TSA package 
NORMALIZE/TSA OBSERVA :VALUE V 		 ! Normalize variance in both light
NORMALIZE/TSA OBSERVB :VALUE V 		 ! curves to the same value of 1
COVAR/TSA OBSERVA OBSERVA AUTOCOVA 1. 0.1 24 LOG
  		 Compute autocov. of `A' 
PLOT/TAB AUTOCOVA :LAG :COVAR 		 ! Plot autocov. function of `A'
COVAR/TSA OBSERVB OBSERVB AUTOCOVB ? ? ? LOG
  		 Compute autocov. of `B' 
PLOT/TAB AUTOCOVB :LAG :COVAR 		 ! Plot autocov. function of `B'
COVAR/TSA OBSERVA OBSERVB CROSSCOV ? ? ? LOG
  		 Compute crosscov. of `A' and `B' 
PLOT/TAB CROSSCOV :LAG :COVAR 		 ! Plot crosscovariance function
          		
! Now you have to fit a common analytic formula to both autocor-
! relation functions, AUTOCOVA and AUTOCOVBB. The MIDAS FIT package 
! or any other suitable tool may be used for this purpose.  
! Choose one of the predefined function forms or code your own 
! function URi, 0 < i < 10, in FORTRAN. Then, the analysis 
! of the delay can proceed: 
          		 
DELAY/TSA OBSERVA OBSERVB CHI2LAG 0 5 200 EXP 0,1,-0.25
! Do Chi2-time lag analysis
PLOT/TAB CHI2LAG :LAG :CHI2 		 ! Plot the results 

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=31 =1 =1993