The two methods are compared on five simulated and five real data sets. This report gives a detailed comparison of two of the forecasting methods (fuzzy forecaster and statistically motivated curve smoothers as forecasters). We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. By implementing a suite of time series modeling and forecasting methods more » and using well-chosen alarm criteria, we reduce the number of false alarms. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The project team selected satellite sensor data to use as the one main example to which its analysis algorithms would be applied. This report describes work during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security (NN) Knowledge Fusion (KF) Project. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. By implementing a suite of time series modeling and forecasting methods and using more » well-chosen alarm criteria, we reduce the number of false alarms.

The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project.

Central alarm station