Box Modeling

Modeling is the process of condensing the raw data into a terse concise model.  SensorMINER utilizes several algorithms to perform this including rule induction, path modeling, and box modeling. Each method has its own advantages and disadvantages producing specific output. Regardless of the modeling method used, the common goal is to build the necessary files required by the anomaly detection engine.

In box modeling, a multidimensional time series is modeled by approximating its path through state phase space with a sequence of boxes. A test point is anomalous if it is outside of any box, with a score depending on the distance from the nearest box. The method may be generalized to multiple training series by merging paths with no additional cost in testing time.

sensorMiner 3D Box Modeling

Click here for more information on Box Modeling: 

Trajectory Boundary Modeling of Time Series for Anomaly Detection

Learning Rules for Time Series Anomaly Detection