Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Variational Learning for Switching State-Space Models
Neural Computation
A least-squares approach to anomaly detection in static and sequential data
Pattern Recognition Letters
Review: A review of novelty detection
Signal Processing
| Hi-index | 0.00 |
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.