  This paper addresses the estimation of a time- varying parameter in a
network. A group of agents sequentially receive noisy signals about the
parameter (or moving target), which does not follow any particular dynamics.
The parameter is not observable to an individual agent, but it is globally
identifiable for the whole network. Viewing the problem with an online
optimization lens, we aim to provide the finite-time or non-asymptotic analysis
of the problem. To this end, we use a notion of dynamic regret which suits the
online, non-stationary nature of the problem. In our setting, dynamic regret
can be recognized as a finite-time counterpart of stability in the mean- square
sense. We develop a distributed, online algorithm for tracking the moving
target. Defining the path-length as the consecutive differences between target
locations, we express an upper bound on regret in terms of the path-length of
the target and network errors. We further show the consistency of the result
with static setting and noiseless observations.
