Mining Multivariate Time-Series Sensor Data to Discover Behavior Envelopes
This paper addresses large-scale regression tasks using a novel combination of greedy input selection and asymmetric cost. Our primary goal is learning envelope functions suitable for automated detection of anomalies in future sensor data. We argue that this new approach can be more effective than traditional techniques, such as static red-line limits, variance-based error bars, and general probability density estimation.