@inproceedings{305966a9d2ab411cada6a9b3baf1f2ae,
title = "Transformed Spiked Covariance Completion for Time Series Estimation",
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.",
keywords = "Denoising, Matrix Completion, Singular Spectrum Analysis, Time Series Estimation",
author = "Benjamin Eng and Zhizhen Zhao and Farzad Kamalabadi and Lara Waldrop",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8461555",
language = "English (US)",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4484--4488",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
address = "United States",
}