The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
A review of symbolic analysis of experimental data
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.
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Cites
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Experiencing SAX: A novel symbolic representation of time series
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-007-0064-z · Cited by 1,618
Recurrence networks—a novel paradigm for nonlinear time series analysis
Donner, R. V., Zou, Y., Donges, J. F., Marwan, N., & Kurths, J. (2010) · New Journal of Physics · doi:10.1088/1367-2630/12/3/033025 · Cited by 619
Recurrence based time series analysis by means of complex network methods
DONNER, R. V., SMALL, M., DONGES, J. F., MARWAN, N., ZOU, Y., XIANG, R., & KURTHS, J. (2011) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127411029021 · Cited by 423
Symbolic time series analysis via wavelet-based partitioning
Rajagopalan, V., & Ray, A. (2006) · Signal Processing · doi:10.1016/j.sigpro.2006.01.014 · Cited by 267
Forbidden patterns, permutation entropy and stock market inefficiency
Zunino, L., Zanin, M., Tabak, B. M., Pérez, D. G., & Rosso, O. A. (2009) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2009.03.042 · Cited by 249
Metadata
- Authors
- Daw, C. S., Finney, C. E. A., & Tracy, E. R.
- Venue
- Review of Scientific Instruments
- Publisher
- AIP Publishing
- Published
- 2003-02-01
- Volume
- 74
- Issue
- 2
- Pages
- 915-930
- ISSN
- 0034-6748 (ISSN-L)
- 1089-7623
- OpenAlex
- W2141514928
- Cited by
- 591
- References
- 72
- FWCI
- 14.87
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
