A common data scenario is the need to remove extraneous, variable background from some other set of signals that are of interest. Conversely, sometimes the goal is to recognize anomalous events that occur in the presence of normal signal variation. In both cases, the key is to identify the portion(s) of the signal that varies within the envelope of normal noise for the sytem versus the signal that substantially differs from noise. CyberSym Technologies has developed such an anomaly filter using methods similar to Kalman filters. This technology operates in near real time and can be applied to single dimensions or multi-dimensional (often correlated) data. Frequently the data progresses as a function of time, but there is nothing in the filter methodology that requires time to be the progression.

Applications include spectroscopy, computer security, sensor interpretation, and geophysical behavior.