Machine Learning is playing a crucial role in the design of intrusion detectors for Industrial Control Systems (ICS). Intrusion Detection Systems (IDS) rely on data obtained from an operational ICS. Such datasets contain multiple time series, one for each process variable. In this work, we explore how such time series can be exploited to understand the effect of time patterns in mining the process invariants, i.e., conditions on process state variables. We use the knowledge gained through the time patterns to determine the optimal data collection size for generating the invariants. The study reported here was conducted using the operational data obtained from a water treatment plant.