Symbolic analysis of indicator time series by quantitative sequence alignment

Symbolic analysis of economic indicators and derived time series offers an advantage of transferring quantitative values into qualitative notions by indexing intervals of numerical data with symbols. While differences in the numerical indicators are routinely measured by subtraction, differences in the symbolic indicators can be compared via more procedural quantitative-scoring schemes, the complexity of which depends on the alphabet size. In effect, the similarity of symbolic data sequence becomes a subtle measure. Upon motivating principles of symbolic analysis, our analysis illustrates how the optimized numerical scoring for alignment schemes may reveal functional and causal relations among the indicator data. The approach of symbolic analysis is particularly suitable for data processing in economics, in which partitioning of resources, competence, information access, or knowledge representation is common by the methodological design.

[1]  Helmut Lütkepohl,et al.  Applied Time Series Econometrics , 2004 .

[2]  Charlene Xie,et al.  Generalized Autoregressive Conditional Heteroskedasticity in Credit Risk Measurement , 2009, 2009 International Conference on Management and Service Science.

[3]  João Meidanis,et al.  Introduction to computational molecular biology , 1997 .

[4]  P. Bühlmann.  Extreme events from the return-volume process: a discretization approach for complexity reduction , 1998 .

[5]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[6]  S. K. Park,et al.  Random number generators: good ones are hard to find , 1988, CACM.

[7]  Eugene W. Myers,et al.  Optimal alignments in linear space , 1988, Comput. Appl. Biosci..

[8]  Georg Dorffner,et al.  The benefit of information reduction for trading strategies , 2002 .

[9]  Webb Miller,et al.  A space-efficient algorithm for local similarities , 1990, Comput. Appl. Biosci..

[10]  Jaakko Astola,et al.  Adaptive denoising and lossy compression of images in transform domain , 1999, J. Electronic Imaging.

[11]  Daniel S. Hirschberg,et al.  Algorithms for the Longest Common Subsequence Problem , 1977, JACM.

[12]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[13]  R. Bellman.  Dynamic programming. , 1957, Science.

[14]  Ingoo Han,et al.  Optimal Multi-Scale Time Series Decomposition for Financial Forecasting using Wavelet Thresholding Techniques , 1999, RSFDGrC.