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Entropy 2017, 19(4), 148; doi:10.3390/e19040148

Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

Department of Mechanical & Nuclear Engineering, Pennsylvania State University, University Park, PA 16802, USA
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Academic Editors: Raúl Alcaraz Martínez and Jose C. Principe
Received: 20 January 2017 / Revised: 17 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications)
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Abstract

This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete) symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series). The main challenges here are: (1) definition of the symbol assignment for the time series; (2) identification of the partitioning segment locations in the signal space of time series; and (3) construction of probabilistic finite-state automata (PFSA) from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning. View Full-Text
Keywords: symbolic dynamics; time series symbolization; mutual information symbolic dynamics; time series symbolization; mutual information
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Li, Y.; Ray, A. Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information. Entropy 2017, 19, 148.

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