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Symbolic analysis of indicator time series by quantitative sequence alignment

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Listed:
  • Yamano, Takuya
  • Sato, Kodai
  • Kaizoji, Taisei
  • Rost, Jan-Michael
  • Pichl, Lukás

Abstract

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.

Suggested Citation

  • Yamano, Takuya & Sato, Kodai & Kaizoji, Taisei & Rost, Jan-Michael & Pichl, Lukás, 2008. "Symbolic analysis of indicator time series by quantitative sequence alignment," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 486-495, December.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:486-495
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    References listed on IDEAS

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    1. Peter Buhlmann, 1998. "Extreme events from the return-volume process: a discretization approach for complexity reduction," Applied Financial Economics, Taylor & Francis Journals, vol. 8(3), pages 267-278.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    Cited by:

    1. Yong Shi & Ye-Ran Tang & Wen Long & Ying-Jie Tian & Wen-Ning Yang, 2018. "Finding Hidden Pattern of Financial Time Series Based on Score Matrix in Sequence Alignment," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(12), pages 1439-1456, December.
    2. Laih, Yih-Wenn, 2014. "Measuring rank correlation coefficients between financial time series: A GARCH-copula based sequence alignment algorithm," European Journal of Operational Research, Elsevier, vol. 232(2), pages 375-382.

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