Transformed Spiked Covariance Completion for Time Series Estimation

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

Abstract

In this paper, we address the problem of estimating a noisy, incomplete time series of a dynamical system with an unknown state evolution. The technique that we will present is transformed spiked covariance completion (TSCC), a matrix completion method for signal estimation. This method exploits the spiked covariance model of the underlying signal to develop a linear estimator that is resilient to noise. We discuss the conditions in the signal model for which this technique is applicable and compare this method against other state-of-the-art time series estimation techniques with a numerical example. Our algorithm gives estimates that are more robust to noise in comparison to the current state-of-the-art techniques that address this same estimation problem.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4484-4488
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Keywords

  • Denoising
  • Matrix Completion
  • Singular Spectrum Analysis
  • Time Series Estimation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Eng, B., Zhao, Z., Kamalabadi, F., & Waldrop, L. (2018). Transformed Spiked Covariance Completion for Time Series Estimation. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 4484-4488). [8461555] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461555