TY - GEN
T1 - Analysis of drifting dynamics with neural network hidden markov models
AU - Kohlmorgen, J.
AU - Müllerc, K. R.
AU - Pawelzik, K.
PY - 1998
Y1 - 1998
N2 - We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method provides prediction experts for the inherent dynamical modes. Then, the trained experts are used in a hidden Markov model that allows to model drifts. An application to physiological wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account.
AB - We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method provides prediction experts for the inherent dynamical modes. Then, the trained experts are used in a hidden Markov model that allows to model drifts. An application to physiological wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account.
UR - http://www.scopus.com/inward/record.url?scp=0006132299&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0006132299
SN - 0262100762
SN - 9780262100762
T3 - Advances in Neural Information Processing Systems
SP - 735
EP - 741
BT - Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PB - Neural information processing systems foundation
T2 - 11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Y2 - 1 December 1997 through 6 December 1997
ER -