Name
Abstract | ||
|---|---|---|
In time-series analysis it is often assumed that observed data can be modelled as being derived from a number of regimes of dynamics, as e.g. in a Switching Kalman Filter (SKF) [8,2]. However, it may not be possible to model all of the regimes, and in this case it can be useful to represent explicitly a `novel' regime. We apply this idea to the Factorial Switching Kalman Filter (FSKF) by introducing an extra factor (the `X-factor') to account for the unmodelled variation. We apply our method to physiological monitoring data from premature infants receiving intensive care, and demonstrate that the model is effective in detecting abnormal sequences of observations that are not modelled by the known regimes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72847-4_1 | IbPRIA (1) |
Keywords | Field | DocType |
condition monitoring,observed data,switching kalman filter,unmodelled variation,known regime,extra factor,premature infant,novelty detection,intensive care,factorial switching kalman filter,time-series analysis,abnormal sequence,time series analysis,kalman filter | Novelty detection,Switching Kalman filter,Pattern recognition,Control theory,Physiological monitoring,Computer science,Factorial,Artificial intelligence,Condition monitoring,Intensive care,Machine learning | Conference |
Volume | ISSN | Citations |
4477 | 0302-9743 | 11 |
PageRank | References | Authors |
0.97 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John A. Quinn | 1 | 128 | 14.49 |
| Christopher K. I. Williams | 2 | 6807 | 631.16 |