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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 windowSET/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 UR
, 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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