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Marine Multiple Time Series Relevance Discovery Based on Complex Network

  • Lei Wang
  • Zongwen Huang
  • Suixiang Shi
  • Kuo Chen
  • Lingyu Xu
  • Gaowei Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Ocean measuring point is an important way to obtain many kinds of marine data. Reasonable layout of ocean measuring points can efficiently obtain marine data. At present, a marine measuring point can acquire multiple types of marine data, only by comprehensively using multiple types of ocean data we can more effectively discover the relationship between various ocean measuring points. This paper proposes a mapping method for fusion marine multiple time series into an image, and uses the similarity between different images to construct a complex network. Also, We build a complex network of marine multiple time series by selecting appropriate thresholds. Compared with the traditional method, the network constructed by our approach can find more accurate rules.

Keywords

Marine multivariate time series Fusion image Complex network Relevance discovery 

Notes

Acknowledgments

This thesis is supported by National Key R&D Program of China (2016YFC1403200)(2016YFC1401900), youth fund project of east china sea branch of state oceanic administration (201614).

References

  1. 1.
    Albert, R., Barabási, A.: Statistical mechanics of complex networks. Rev. Modern Phys. 74(1), xii (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)Google Scholar
  3. 3.
    Barthálemy, M., Barrat, A., Pastor-Satorras, R., Vespignani, A.: Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Phys. Rev. Lett. 92(17), 178701 (2004)Google Scholar
  4. 4.
    Boginski, V., Butenko, S., Pardalos, P.M.: Mining market data: a network approach. Comput. Oper. Res. 33(11), 3171–3184 (2006)CrossRefGoogle Scholar
  5. 5.
    Caldarelli, G., Battiston, S., Garlaschelli, D., Catanzaro, M.: Emergence of complexity in financial networks. Lect. Notes Phys. 650, 399–423 (2004)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Colizza, V., Barrat, A., Barthélemy, M., Vespignani, A.: The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl. Acad. Sci. U.S.A. 103(7), 2015–2020 (2006)CrossRefGoogle Scholar
  7. 7.
    Brockmann, D., Helbing, D.: The hidden geometry of complex, network-driven contagion phenomena. Science (New York, N.Y.) 342(6164), 1337–1342 (2013)CrossRefGoogle Scholar
  8. 8.
    Eliazar, I., Koren, T., Klafter, J.: Searching circular dna strands. J. Phys. Condens. Matter 19(6), 160–164 (2007)CrossRefGoogle Scholar
  9. 9.
    Gómezgardeñes, J., Latora, V., Moreno, Y., Profumo, E.: Spreading of sexually transmitted diseases in heterosexual populations. Proc. Natl. Acad. Sci. U.S.A. 105(5), 1399–1404 (2008)CrossRefGoogle Scholar
  10. 10.
    Karl, D.M., Michaels, A.F.: The Hawaiian ocean time-series (hot) and bermuda atlantic time-series study (bats). Deep. Sea Res. Part II Top. Stud. Ocean. 43(2–3), 127–128 (1996)CrossRefGoogle Scholar
  11. 11.
    Kobayashi, M., Okamoto, Y.: Submodularity of minimum-cost spanning tree games, pp. 231–238 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296(5569), 910–913 (2002)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.: Spread of epidemic disease on networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys.66(1 Pt 2), 016128 (2002)Google Scholar
  14. 14.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Nodoushan, E.J.: Monthly forecasting of water quality parameters within bayesian networks: A case study of honolulu, pacific ocean. Civil Eng. J. 4(1), 188 (2018)Google Scholar
  16. 16.
    Guimera, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Nature 433(7028), 895 (2005)CrossRefGoogle Scholar
  17. 17.
    Boccaletti, S., Latora, V., Moreno, Y., Chavezf, M., Hwang, D.-U.: Complex networks: structure and dynamics. Complex Syst. Complex. Sci. 424(4-5), 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Stanley, H.E., et al.: Self-organized complexity in economics and finance. Proc. Natl. Acad. Sci. U.S.A. 99(3), 2561–2565 (2002)CrossRefGoogle Scholar
  19. 19.
    Tumminello, M., Matteo, T.D., Aste, T., Mantegna, R.N.: Correlation based networks of equity returns sampled at different time horizons. Eur. Phys. J. B 55(2), 209–217 (2007)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang, Z., Oates, T.: Imaging time-series to improve classification and imputation. arXiv preprint arXiv:1506.00327 (2015)
  21. 21.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity, vol. 13, pp. 600–612. IEEE (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lei Wang
    • 1
  • Zongwen Huang
    • 2
  • Suixiang Shi
    • 1
  • Kuo Chen
    • 3
  • Lingyu Xu
    • 2
  • Gaowei Zhang
    • 2
  1. 1.National Marine Data and Information ServiceTianjinChina
  2. 2.Shanghai UniversityShanghaiChina
  3. 3.Tongji UniversityShanghaiChina

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