A Family of Algorithms for Finding Temporal Structure in Data
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:371-378, 1997.
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.