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.
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© 2007 Springer Berlin Heidelberg
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Quinn, J.A., Williams, C.K.I. (2007). Known Unknowns: Novelty Detection in Condition Monitoring. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_1
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DOI: https://doi.org/10.1007/978-3-540-72847-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72846-7
Online ISBN: 978-3-540-72847-4
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