Active measurements on network paths provide end-to-end network health status in terms of metrics such as bandwidth, delay, jitter and loss. Hence, they are increasingly being used for various network control and management functions on the Internet. For purposes of network health anomaly detection and forecasting involved in these functions, it is important to accurately model the time-series process of active measurements. In this paper, we describe our time-series analysis of two typical active measurement data sets collected over several months: (i) routine, and (ii) event-laden. Our analysis suggests that active network measurements follow the moving average process. Specifically, they possess ARIMA(0,1,q) model characteristics with low q values, across multi-resolution timescales. We validate our model selection accuracy by comparing how well our predicted values using our model match the actual measurements.