A Family of Algorithms for Finding Temporal Structure in Data
T. Oates, M. Schmill, D. Jensen, and P.R. Cohen. "A Family of
Algorithms for Finding Temporal Structure in Data." Preliminary
Papers of the Sixth International Workshop on Artificial Intelligence
and Statistics. pp. 371-378.
- Abstract
- Finding patterns in temporally structured data is an important
and difficult problem. Examples of temporally structured data
include time series of economic indicators, distributed network
status reports, and continuous streams such as flight recorder
data. We have developed a family of algorithms for finding structure
in multivariate, discrete-valued time series data (Oates & Cohen
1996b; Oates, Schmill, & Cohen 1996; Oates et al. 1995). In this
paper, we introduce a new member of that family for handling event-based
data, and offer an empirical characterization of a time series
based algorithm.
- Text
- A Postscript version of this paper is available on request.
Fineprint