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.

A Linguistic Approach to the Analysis of Accelerometer Data for Gait Analysis

“…Also, symbolic data is often less sensitive to measurement noise. Although it is not obvious that a symbolic approach is useful when dealing with systems having continuous state spaces, many previous works have used symbolic transformation of continuous data with great success [18]. Another advantage of Previous linguistic approaches have expressed visual data in terms of symbols, then classified patterns into one of a few pre-defined labeled patterns (activities).…”

Mapping the complexity of ecological models

“…No standard method is available in the literature to tackle either problem. For a nice review of symbolisation methods and their application we refer the reader to Daw et al, (2003). By far, the most widespread approach in the literature is to use a binary alphabet.…”

Entropy as a Measurement for the Quality of Demand Forecasting

“…But this strongly depends on the choice of the transformation. Due to the reduction of details, the analyses of symbol sequences are less vulnerable to noise (Daw et al, 2003) and, consequently, conclusions drawn from these sequences are more precise.…”

“…The static transformation is realized by choosing one (in the binary case) or more thresholds and the different symbols are then assigned to the intervals between them. There are diverse rules to calculate those thresholds, e.g., data mean or median value (Daw et al, 2003). The dynamic transformation is preferred when the dynamics are more important than the absolute values.…”

“…The maximum possible word length strongly depends on the alphabet size and the length of the time-series: The larger the alphabet and the shorter the time-series, the smaller is the possible word length (and vice versa) (Daw et al, 2003). Since real time-series are in general rather short, we use a binary alphabet to be able to get a word length up to L=5.…”