Time-series analysis with small and faulty data: L1-norm decompositions of Hankel matrices

Georgios I. Orfanidis; Dimitris A. Pados; George Sklivanitis

doi:10.1117/12.2619243

31 May 2022 Time-series analysis with small and faulty data: L1-norm decompositions of Hankel matrices

Georgios I. Orfanidis, Dimitris A. Pados, George Sklivanitis

Author Affiliations +

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970B (2022) https://doi.org/10.1117/12.2619243
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In the rapidly advancing field of autonomous systems, real-time operation and monitoring in non-stationary environments frequently relies on analysis (filtering/prediction) of short sequences of sensed data that may be partly unreliable, missing, or faulty. Hankel-matrix representation and decomposition is a model-free approach that is becoming increasingly popular for the analysis of time-series data taking advantage of the progress in linear algebra methods in past years. In this work, we establish that novel L1-norm decompositions of Hankel matrices offer sturdy resistance against partially faulty sensed sequences and, therefore, creates a strong new framework for robust real-time monitoring of autonomous systems. The findings in this paper are illustrated and supported by extensive experimentation on artificial data.

Conference Presentation

Citation Download Citation

Georgios I. Orfanidis, Dimitris A. Pados, and George Sklivanitis "Time-series analysis with small and faulty data: L1-norm decompositions of Hankel matrices", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970B (31 May 2022); https://doi.org/10.1117/12.2619243

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