The present invention relates generally to building management systems.  The present invention relates more particularly to detecting outliers in time-series data in a building management system.  The present invention relates more particularly still to systems and methods for retraining outlier detection limits used to detect outliers in time-series data in a building management system. 
A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area.  A BMS can include a heating, ventilation, and air conditioning (HVAC) system, a security system, a lighting system, a fire alerting system, another system that is capable of managing building functions or devices, or any combination thereof.  BMS devices may be installed in any environment (e.g., an indoor area or an outdoor area) and the environment may include any number of buildings, spaces, zones, rooms, or areas.  A BMS may include METASYS® building controllers or other devices sold by Johnson Controls, Inc., as well as building devices and components from other sources. 
In HVAC systems, temporal (time-series) processes such as temperatures and flows exhibit statistical characteristics that provide information about how the system is performing in terms of error from its setpoint.  These processes can be examined to expose when the system begins to degrade in performance to alert the user to repair a fault before it becomes more severe.  For example, data from the temporal process can be compared to outlier detection limits to determine whether a statistically significant deviation from the setpoint has occurred.  It can be difficult and challenging to determine appropriate outlier detection limits for various controlled processes.