  Some of the most important results in prediction theory and time series
analysis when finitely many values are removed from or added to its infinite
past have been obtained using difficult and diverse techniques ranging from
duality in Hilbert spaces of analytic functions (Nakazi, 1984) to linear
regression in statistics (Box and Tiao, 1975). We unify these results via a
finite-dimensional duality lemma and elementary ideas from the linear algebra.
The approach reveals the inherent finite-dimensional character of many
difficult prediction problems, the role of duality and biorthogonality for a
finite set of random variables. The lemma is particularly useful when the
number of missing values is small, like one or two, as in the case of
Kolmogorov and Nakazi prediction problems. The stationarity of the underlying
process is not a requirement. It opens up the possibility of extending such
results to nonstationary processes.
