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Equilibrium Resolution for Epoch Partitioning

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Artificial Intelligence Applications and Innovations (AIAI 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 647))

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Abstract

This paper proposes a method for determining the resolution for the processing of irregularly-sampled time series data to provide a balanced perspective of agents’ behaviour. The behaviour is described as a collection of prolonged events, which are characterised by start/open and end/close times in addition to other useful attributes. We propose the definition of an equilibrium resolution and carry out its analysis based on probabilistic assumptions. The resulting methods of determining the equilibrium resolution are tested on real-life time series data sets from the Financial and Travel problem domains.

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References

  1. Al-baghdadi, N., Wisniewski, W., Lindsay, D., Lindsay, S., Kalnishkan, Y., Watkins, C.: Structuring time series data to gain insight into agent behaviour. In: 2019 IEEE International Conference on Big Data, pp. 5480–5490 (2019)

    Google Scholar 

  2. Al-Hmouz, R., Pedrycz, W., Balamash, A., Morfeq, A.: Granular representation schemes of time series: a study in an optimal allocation of information granularity. In: 2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI), pp. 44–51 (2013)

    Google Scholar 

  3. Arandia, E., Eck, B., McKenna, S.: The effect of temporal resolution on the accuracy of forecasting models for total system demand. Procedia Eng. 89, 916–925 (2014). 16th Water Distribution System Analysis Conference, WDSA2014

    Google Scholar 

  4. Athanasopoulos, G., Hyndman, R.J., Kourentzes, N., Petropoulos, F.: Forecasting with temporal hierarchies. Eur. J. Oper. Res. 262(1), 60–74 (2017)

    Article  MathSciNet  Google Scholar 

  5. Challet, D., Chicheportiche, R., Lallouache, M., Kassibrakis, S.: Statistically validated leadlag networks and inventory prediction in the foreign exchange market. Adv. Complex Syst. 22 p. (2018)

    Google Scholar 

  6. Krings, G., Karsai, M., Bernhardsson, S., Blondel, V.D., Saramäki, J.: Effects of time window size and placement on the structure of an aggregated communication network. EPJ Data Sci. 1(1), 1–16 (2012). https://doi.org/10.1140/epjds4

    Article  Google Scholar 

  7. Lindsay, D.: Fxclienttrades. https://www.kaggle.com/davidlindsay1979/toptradingclientdata/kernels

  8. Nason, G.P., Powell, B., Elliott, D., Smith, P.A.: Should we sample a time series more frequently?: decision support via multirate spectrum estimation. J. R. Stat. Soc. Ser. A (Stat. Soc.) 180(2), 353–407 (2017)

    Article  MathSciNet  Google Scholar 

  9. Sulo, R., Berger-Wolf, T., Grossman, R.: Meaningful selection of temporal resolution for dynamic networks. In: Proceedings of the Eighth Workshop on Mining and Learning with Graphs, MLG 2010, pp. 127–136. Association for Computing Machinery, New York (2010)

    Google Scholar 

  10. Tumminello, M., Miccichè, S., Lillo, F., Piilo, J., Mantegna, R.N.: Statistically validated networks in bipartite complex systems. PLoS ONE 6(3), e17994 (2011)

    Article  Google Scholar 

  11. Wong, C.: Nyc taxi trips. http://www.andresmh.com/nyctaxitrips/

  12. Wu, X., Shi, B., Dong, Y., Huang, C., Faust, L., Chawla, N.: Restful: resolution-aware forecasting of behavioral time series data, pp. 1073–1082 (2018)

    Google Scholar 

  13. Xue-dong, G., Chen, H.: The method of time granularity determination on time series based on structural similarity measure algorithm. In: The International Symposium on the Analytic Hierarchy Process (ISAHP) (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Royal Holloway, University of London, Egham, UK

    Wojciech Wisniewski & Yuri Kalnishkan

  2. Algorithmic Laboratories Ltd., Ocean House, Bracknell, Berkshire, RG12 1AX, UK

    David Lindsay & Siân Lindsay

Authors
  1. Wojciech Wisniewski
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  2. Yuri Kalnishkan
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  3. David Lindsay
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  4. Siân Lindsay
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Corresponding authors

Correspondence to Wojciech Wisniewski or Yuri Kalnishkan .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John Macintyre

  4. Universidade do Minho, Guimaraes, Portugal

    Paulo Cortez

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© 2022 IFIP International Federation for Information Processing

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Wisniewski, W., Kalnishkan, Y., Lindsay, D., Lindsay, S. (2022). Equilibrium Resolution for Epoch Partitioning. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_32

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  • DOI: https://doi.org/10.1007/978-3-031-08337-2_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08336-5

  • Online ISBN: 978-3-031-08337-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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