  An important feature of all real-world networks is that the network structure
changes over time. Due to this dynamic nature, it becomes difficult to propose
suitable growth models that can explain the various important characteristic
properties of these networks. In fact, in many application oriented studies
only knowing these properties is sufficient. We, in this paper show that even
if the network structure at a future time point is not available one can still
manage to estimate its properties. We propose a novel method to map a temporal
network to a set of time series instances, analyze them and using a standard
forecast model of time series, try to predict the properties of a temporal
network at a later time instance. We mainly focus on the temporal network of
human face- to-face contacts and observe that it represents a stochastic
process with memory that can be modeled as ARIMA. We use cross validation
techniques to find the percentage accuracy of our predictions. An important
observation is that the frequency domain properties of the time series obtained
from spectrogram analysis could be used to refine the prediction framework by
identifying beforehand the cases where the error in prediction is likely to be
high. This leads to an improvement of 7.96% (for error level <= 20%) in
prediction accuracy on an average across all datasets. As an application we
show how such prediction scheme can be used to launch targeted attacks on
temporal networks.
