Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997 |
| Publisher | Neural information processing systems foundation |
| Pages | 735-741 |
| Number of pages | 7 |
| ISBN (Print) | 0262100762, 9780262100762 |
| Publication status | Published - 1998 |
| Event | 11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States Duration: 1997 Dec 1 → 1997 Dec 6 |
Publication series
| Name | Advances in Neural Information Processing Systems |
|---|---|
| ISSN (Print) | 1049-5258 |
Other
| Other | 11th Annual Conference on Neural Information Processing Systems, NIPS 1997 |
|---|---|
| Country | United States |
| City | Denver, CO |
| Period | 97/12/1 → 97/12/6 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Fingerprint Dive into the research topics of 'Analysis of drifting dynamics with neural network hidden markov models'. Together they form a unique fingerprint.
Cite this
Kohlmorgen, J., Müllerc, K. R., & Pawelzik, K. (1998). Analysis of drifting dynamics with neural network hidden markov models. In Advances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997 (pp. 735-741). (Advances in Neural Information Processing Systems). Neural information processing systems foundation.