Evolutionary learning of dynamic probabilistic models with large time lags
✍ Scribed by Allan Tucker; Xiaohui Liu; Andrew Ogden-Swift
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 322 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0884-8173
- DOI
- 10.1002/int.1027
No coin nor oath required. For personal study only.
✦ Synopsis
In this paper, we explore the automatic explanation of multivariate time series MTS Ž . through learning dynamic Bayesian networks DBNs . We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations.