Spectral estimation based on compressibility.

Nagaraj, Nithin (2023) Spectral estimation based on compressibility. In: Perspectives in Nonlinear Dynamics 2023 (PNLD 2023),, August 1-4, 2023., Centre for Complex Systems & Dynamics, IIT Madras and the Department of Applied Mechanics.

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Contribution	Name	Email
Collaborator	Kathpalia, Aditi	*

Abstract:	Signal processing and information theory [1] are two disparate fields used for characterizing signals for various scientific and engineering applications. Spectral analysis (a subset of signal processing) helps estimation of power at different frequency components present in the signal. Characterizing a time-series based on its average amount of information is useful for estimating its complexity and compressibility (eg., for communication applications). Information theory doesn’t deal with spectral content while signal proessing doesn’t consider the information content or compressibility of the signal. Data compression is closely tied to information theory and allows for an algorithmic approach to estimate complexity and compressibility of time series. In this work, we attempt to bring the fields of signal processing and information theory together by using a lossless data compression algorithm to estimate the amount of information or ‘compressibility’ of time series at different scales or frequencies. We employ the Effort-to-Compress (ETC) algorithm [2] to obtain a Compression Spectrum
Item Type:	Conference or Workshop Item (Paper)
Subjects:	School of Natural and Engineering Sciences > Complex Systems
Divisions:	Schools > Natural Sciences and Engineering
Date Deposited:	10 Feb 2026 10:11
Last Modified:	10 Feb 2026 10:12
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URI:	http://eprints.nias.res.in/id/eprint/3132

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