  A new method is proposed to compute connectivity measures on multivariate
time series with gaps. Rather than removing or filling the gaps, the rows of
the joint data matrix containing empty entries are removed and the calculations
are done on the remainder matrix. The method, called measure adapted gap
removal (MAGR), can be applied to any connectivity measure that uses a joint
data matrix, such as cross correlation, cross mutual information and transfer
entropy. MAGR is favorably compared using these three measures to a number of
known gap-filling techniques, as well as the gap closure. The superiority of
MAGR is illustrated on time series from synthetic systems and financial time
series.
