In this presentation we present a method and metrics to enhance network Situational Awareness. Since network Situational Awareness (SA) is primarily concerned with monitoring trends and changes in traffic patterns the analysis of traffic over time or time series data is a key element. Therefore metrics related to time series analysis play an important role in SA. In particular correlations over time or the autocorrelation functions need to be analyzed. However these correlations the autocorrelation function and other time series metrics need to be interpreted with respect to the time window and time scale being considered. The presentation will discuss the autocorrelation function under different time scales and the inferences we can make from it. The inferences from changes in the autocorrelation with changes in time scales can shed light on the presence of short-term or long-term dependencies in traffic patterns. This issue has been identified as important for anomaly/intrusion detection in the literature. We report on the findings from an analysis of flow data that investigates this issue. We first construct an initial time series of traffic volumes over a given time window. Then we estimate the autocorrelation function for this series. Next we vary the time scale and estimate the corresponding autocorrelation functions for these new time series. Finally we compare these autocorrelation functions in relation to their time scales and develop a metric to quantify the differences. This metric can be tracked over time that is over successive time windows. This approach could detect attacks and intrusions that do not perturb the network traffic in other discernible ways and thus may not be identified early enough by other detection techniques to enable effective mitigation. This method also allows us to distinguish between short-term and long-term dependencies within the traffic patterns. This distinction is important for selecting the appropriate techniques for further analyzing network traffic. The analyses are illustrated with publicly available data.