A Family of Algorithms for Finding Temporal Structure in Data

Tim Oates, Matthew J. Schmill, David Jensen, Paul R. Cohen
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:371-378, 1997.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-oates97a, title = {A Family of Algorithms for Finding Temporal Structure in Data}, author = {Oates, Tim and Schmill, Matthew J. and Jensen, David and Cohen, Paul R.}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {371--378}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/oates97a/oates97a.pdf}, url = {https://proceedings.mlr.press/r1/oates97a.html}, 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.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T A Family of Algorithms for Finding Temporal Structure in Data %A Tim Oates %A Matthew J. Schmill %A David Jensen %A Paul R. Cohen %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-oates97a %I PMLR %P 371--378 %U https://proceedings.mlr.press/r1/oates97a.html %V R1 %X 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. %Z Reissued by PMLR on 30 March 2021.
APA
Oates, T., Schmill, M.J., Jensen, D. & Cohen, P.R.. (1997). A Family of Algorithms for Finding Temporal Structure in Data. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:371-378 Available from https://proceedings.mlr.press/r1/oates97a.html. Reissued by PMLR on 30 March 2021.

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