Abstract
The problem of multiple change point estimation is considered for sequences with unknown number of change points. A consistency framework is suggested that is suitable for highly dependent time-series, and an asymptotically consistent algorithm is proposed. In order for the consistency to be established the only assumption required is that the data is generated by stationary ergodic time-series distributions. No modeling, independence or parametric assumptions are made; the data are allowed to be dependent and the dependence can be of arbitrary form. The theoretical results are complemented with experimental evaluations.
| Original language | English |
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| Title of host publication | Advances in Neural Information Processing Systems 25 |
| Subtitle of host publication | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
| Pages | 3086-3094 |
| Number of pages | 9 |
| Publication status | Published - 1 Dec 2012 |
| Externally published | Yes |
| Event | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: 3 Dec 2012 → 6 Dec 2012 |
Publication series
| Name | Advances in Neural Information Processing Systems |
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| Volume | 4 |
| ISSN (Print) | 1049-5258 |
Conference
| Conference | 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 |
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| Country/Territory | United States |
| City | Lake Tahoe, NV |
| Period | 3/12/12 → 6/12/12 |