A review of symbolic analysis of experimental data Export

@article{Daw2003, abstract = {This review covers the group of data-analysis techniques collectively referred to as symbolization or symbolic time-series analysis. Symbolization involves transformation of raw time-series measurements (i.e., experimental signals) into a series of discretized symbols that are processed to extract information about the generating process. In many cases, the degree of discretization can be quite severe, even to the point of converting the original data to single-bit values. Current approaches for constructing symbols and detecting the information they contain are summarized. Novel approaches for characterizing and recognizing temporal patterns can be important for many types of experimental systems, but this is especially true for processes that are nonlinear and possibly chaotic. Recent experience indicates that symbolization can increase the efficiency of finding and quantifying information from such systems, reduce sensitivity to measurement noise, and discriminate both specific and general classes of proposed models. Examples of the successful application of symbolization to experimental data are included. Key theoretical issues and limitations of the method are also discussed.}, author = {Daw, C. S. and Finney, C. E. A. and Tracy, E. R.}, citeulike-article-id = {2943127}, citeulike-linkout-0 = {http://dx.doi.org/10.1063/1.1531823}, citeulike-linkout-1 = {http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2003RScI...74..915D}, doi = {10.1063/1.1531823}, journal = {Review of Scientific Instruments}, keywords = {nonlinear}, month = {February}, pages = {915--930}, posted-at = {2008-06-30 06:49:31}, priority = {2}, title = {A review of symbolic analysis of experimental data}, url = {http://dx.doi.org/10.1063/1.1531823}, volume = {74}, year = {2003} }

TY - JOUR ID - Daw2003 L3 - citeulike-article-id:2943127 N2 - This review covers the group of data-analysis techniques collectively referred to as symbolization or symbolic time-series analysis. Symbolization involves transformation of raw time-series measurements (i.e., experimental signals) into a series of discretized symbols that are processed to extract information about the generating process. In many cases, the degree of discretization can be quite severe, even to the point of converting the original data to single-bit values. Current approaches for constructing symbols and detecting the information they contain are summarized. Novel approaches for characterizing and recognizing temporal patterns can be important for many types of experimental systems, but this is especially true for processes that are nonlinear and possibly chaotic. Recent experience indicates that symbolization can increase the efficiency of finding and quantifying information from such systems, reduce sensitivity to measurement noise, and discriminate both specific and general classes of proposed models. Examples of the successful application of symbolization to experimental data are included. Key theoretical issues and limitations of the method are also discussed. JF - Review of Scientific Instruments EP - 930 TI - A review of symbolic analysis of experimental data VL - 74 SP - 915 KW - nonlinear AU - Daw, CS AU - Finney, CEA AU - Tracy, ER PY - 2003/February// UR - http://dx.doi.org/10.1063/1.1531823 ER -