Testing time symmetry in time series using data compression dictionaries

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volumn 69, Issue 5 1, 2004, Pages

  Kennel, Matthew B ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHAOS THEORY; COMPUTER SIMULATION; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; MONTE CARLO METHODS; NOISE ABATEMENT; PARAMETER ESTIMATION; RANDOM PROCESSES; ROBUSTNESS (CONTROL SYSTEMS); SIGNAL PROCESSING; TIME SERIES ANALYSIS; TOPOLOGY; TRANSFER FUNCTIONS;

DATA SETS; IRREVERSIBILITY; NONLINEAR TRANSFORMATIONS; TIME SYMMETRY;

DATA COMPRESSION;

EID: 37649031328     PISSN: 15393755     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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16. sgn(·)=-1,0,1 for negative, zero, and positive arguments.
17. Statistically significant θ̂<0, i.e., small 1-p, may imply some kind of unmodeled nonstationarity. In that case, the data set ought to be broken up into more, shorter, interleaved training and test sets, accumulated and repeated.
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22. In common computer practice the class of compression software generally known as "zip" uses dictionary-based universal compression methods.
23. See EPAPS Document No. E-PLEEE8-69-140404 for the complete source code in C++. A direct link to this document may be found in the online article's HTML reference section. This document may also be reached via the EPAPS homepage (http://www.aip.org/pubservs/epaps.html) or from ftp.aip.org in the directory /epaps/. See the EPAPS homepage for more information.