Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation

Nurali Virani, Ji Woong Lee, Shashi Phoha, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes a nonparametric density estimation-based information-space partitioning and symbolization technique for capturing and representing the underlying statistical behavior in dynamic data-driven application systems (DDDAS). In contrast with existing tools that address alphabet-size selection and partitioning in two separate steps, the proposed technique jointly determines both the number of symbols (i.e., the alphabet size) and the regions associated with these symbols (i.e., the information-space partition) in a single step. In order to validate the technique, dynamic data-driven models have been developed from time series of vector-valued measurements extracted from a simulation testbed, and are compared with models that rely on Gaussian mixture modeling for information-space partitioning.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3328-3333
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

Fingerprint

Time series
Testbeds

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Virani, N., Lee, J. W., Phoha, S., & Ray, A. (2016). Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation. In 2016 American Control Conference, ACC 2016 (pp. 3328-3333). [7525431] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525431
Virani, Nurali ; Lee, Ji Woong ; Phoha, Shashi ; Ray, Asok. / Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation. 2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 3328-3333 (Proceedings of the American Control Conference).
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abstract = "This paper proposes a nonparametric density estimation-based information-space partitioning and symbolization technique for capturing and representing the underlying statistical behavior in dynamic data-driven application systems (DDDAS). In contrast with existing tools that address alphabet-size selection and partitioning in two separate steps, the proposed technique jointly determines both the number of symbols (i.e., the alphabet size) and the regions associated with these symbols (i.e., the information-space partition) in a single step. In order to validate the technique, dynamic data-driven models have been developed from time series of vector-valued measurements extracted from a simulation testbed, and are compared with models that rely on Gaussian mixture modeling for information-space partitioning.",
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Virani, N, Lee, JW, Phoha, S & Ray, A 2016, Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation. in 2016 American Control Conference, ACC 2016., 7525431, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 3328-3333, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7525431

Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation. / Virani, Nurali; Lee, Ji Woong; Phoha, Shashi; Ray, Asok.

2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3328-3333 7525431 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper proposes a nonparametric density estimation-based information-space partitioning and symbolization technique for capturing and representing the underlying statistical behavior in dynamic data-driven application systems (DDDAS). In contrast with existing tools that address alphabet-size selection and partitioning in two separate steps, the proposed technique jointly determines both the number of symbols (i.e., the alphabet size) and the regions associated with these symbols (i.e., the information-space partition) in a single step. In order to validate the technique, dynamic data-driven models have been developed from time series of vector-valued measurements extracted from a simulation testbed, and are compared with models that rely on Gaussian mixture modeling for information-space partitioning.

AB - This paper proposes a nonparametric density estimation-based information-space partitioning and symbolization technique for capturing and representing the underlying statistical behavior in dynamic data-driven application systems (DDDAS). In contrast with existing tools that address alphabet-size selection and partitioning in two separate steps, the proposed technique jointly determines both the number of symbols (i.e., the alphabet size) and the regions associated with these symbols (i.e., the information-space partition) in a single step. In order to validate the technique, dynamic data-driven models have been developed from time series of vector-valued measurements extracted from a simulation testbed, and are compared with models that rely on Gaussian mixture modeling for information-space partitioning.

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Virani N, Lee JW, Phoha S, Ray A. Information-space partitioning and symbolization of multi-dimensional time-series data using density estimation. In 2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 3328-3333. 7525431. (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2016.7525431