Skip to main content
SpringerLink
Log in

Use of the Fractal Analysis of Non-stationary Time Series in Mobile Foreign Exchange Trading for M-Learning

  • Conference paper
  • First Online:
Internet of Things, Infrastructures and Mobile Applications (IMCL 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1192))

Included in the following conference series:

Abstract

Mobile foreign exchange trading system for m-learning is proposed. It’s used for time series analysis skills learning. Method of pre-forecasting fractal R/S analysis of non-stationary time series is integrated in system. This method includes: persistence, anti-persistence and random level determination based on the calculation of the Hurst exponent. To calculate the average value of the nonperiodic cycle of time series, as well as to establish the potential profitable of assets that are represented by financial time series. A criterion for determination of the average length of non-periodic cycles based on the smoothing of V-statistics with simple moving average and Kaufman’s adaptive moving average is proposed. It has been confirmed that most financial time series are more or less persistent and endowed with long-term memory of their initial conditions using computer simulation. Time series of course pairs are close to random. Using fractal analysis in m-learning mobile foreign exchange trading systems for smartphones based on iOS or Android operating systems is suggested. The system is characterized by visualization and description of all stages, which has to be executed for time series analysis. Practical use of this system has shown high efficiency for time series analysis skills learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mandelbrot, B.: Statistical methodology for non-periodic cycles: from the covariance to R/S analysis. Ann. Econ. Soc. Measur. 1, 259–290 (1972)

    Google Scholar 

  2. Mandelbrot, B.B., Hudson, R.L.: The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin and Reward. Basic Books, New York (2004)

    MATH  Google Scholar 

  3. Peters, E.E.: Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley, New York (1994)

    Google Scholar 

  4. Feder, J.: Fractals. Springer US, New York (1988). https://doi.org/10.1007/978-1-4899-2124-6

  5. Georgiev, T., Georgieva, E., Smrikarov, A.: M-learning – a new stage of e-learning. In: International Conference on Computer Systems and Technologies, p. 28 (2004)

    Google Scholar 

  6. Peng, C.-K., Buldyrev, S.V., Havtin, S., Simons, M., Stanley, H.E., Goldberger, A.L.: Mosaic organization of DNA nucleotides. Phys. Rev. E 49(2), 1685–1689 (1994)

    Article  Google Scholar 

  7. Peng, C.-K., Havlin, S., Stanley, H.E., Goldberger, A.L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995). https://doi.org/10.1063/1.166141

    Article  Google Scholar 

  8. Kantelhardt, J.W.: Fractal and multifractal time series. In: Meyers, R. (eds.) Mathematics of Complexity and Dynamical Systems, pp. 463–487. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-1806-1_30

  9. Kyrychenko, L., Deineko, Z.: Estimating the self-similarity of a stochastic time series using the wavelet analysis method. Radioelectron. Comput. Syst. 4(38), 99–105 (2009)

    Google Scholar 

  10. Parzen, E.: Long memory of statistical time series modeling. In: NBER/NSF Time Series Conference, p. 10. Texas A&M University (2004)

    Google Scholar 

  11. Nayak, R., te Braak, P.: Temporal pattern matching for the prediction of stock prices. In: 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), Australia, pp. 95–103 (2007)

    Google Scholar 

  12. Singh, S.: Pattern modeling in time-series forecasting. Cybern. Syst. 31(1), 49–65 (2000). https://doi.org/10.1080/019697200124919

    Article  MATH  Google Scholar 

  13. Kuchansky, A., Biloshchytskyi, A.: Selective pattern matching method for time-series forecasting. Eastern-Eur. J. Enterpr. Technol. 6(4(78)), 13–18 (2015). https://doi.org/10.15587/1729-4061.2015.54812

  14. Perlin, M.S.: Nearest neighbor method. Rev. Eletrôn. de Administr. 13(2), 15 (2007)

    Google Scholar 

  15. Fernández-Rodríguez, F., Sosvilla-Rivero, S., Andrada-Félix, J.: Nearest-neighbour predictions in foreign exchange markets. FEDEA Working Paper No. 2002-05 (2002). https://doi.org/10.2139/ssrn.300404

  16. Kahveci, T., Singh, A.: Variable length queries for time series data. In: Proceedings of the 17th International Conference on Data Engineering, pp. 273–282 (2001). https://doi.org/10.1109/icde.2001.914838

  17. Li, C., Chang, E., Garcia-Molina, H., Wiederhold, G.: Clustering for approximate similarity search in high-dimensional spaces. IEEE Trans. Knowl. Data Eng. 14(4), 792–808 (2002). https://doi.org/10.1109/tkde.2002.1019214

    Article  Google Scholar 

  18. Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., Dubnytska, A., Vatskel, V.: The method of the scientific directions potential forecasting in infocommunication systems of an assessment of the research activity results. In: 2017 IEEE International Conference « Problems of Infocommunications. Science and Technology » (PIC S&T), pp. 69–72 (2017). https://doi.org/10.1109/infocommst.2017.8246352

  19. Biloshchytskyi, A., et al.: A method for the identification of scientists’ research areas based on a cluster analysis of scientific publications. Eastern-Eur. J. Enterpr. Technol. 5(2(89)), 4–10 (2017). https://doi.org/10.15587/1729-4061.2017.112323

  20. Biloshchytskyi, A., Kuchansky, A., Andrashko, Yu., Biloshchytska, S., Kuzka, O., Terentyev, O.: Evaluation methods of the results of scientific research activity of scientists based on the analysis of publication citations. Eastern-Eur. J. Enterpr. Technol. 3(2(87)), 4–10 (2017). https://doi.org/10.15587/1729-4061.2017.103651

  21. Otradskaya, T., Gogunskii, V., Antoshchuk, S., Kolesnikov, O.: Development of parametric model of prediction and evaluation of the quality level of educational institutions. Eastern-Eur. J. Enterpr. Technol. 5(3(83)), 12–21 (2016). https://doi.org/10.15587/1729-4061.2016.80790

  22. Biloshchytskyi, A., et al.: A method to evaluate the scientific activity quality of HEIs based on a scientometric subjects presentation model. Eastern-Eur. J. Enterpr. Technol. 6(2(90)), 16–22 (2017). https://doi.org/10.15587/1729-4061.2017.118377

  23. Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Biloshchytska, S., Chala, L.: Detection of near duplicates in tables based on the locality-sensitive hashing method and the nearest neighbor method. Eastern-Eur. J. Enterpr. Technol. 6(4(84)), 4–10 (2016). https://doi.org/10.15587/1729-4061.2016.86243

  24. Biloshchytskyi, A., Kuchansky, A., Biloshchytska, S., Dubnytska, A.: Conceptual model of automatic system of near duplicates detection in electronic documents. In: 2017 IEEE International Conference « The Experience of Designing and Application of CAD Systems in Microelectronics » (CADSM), pp. 381–384 (2017). https://doi.org/10.1109/cadsm.2017.7916155

  25. Vercellis, C.: Business Intelligence: Data Mining and Optimization for Decision Making, p. 417. Wiley, Cornwall (2009). https://doi.org/10.1002/9780470753866

  26. Lukashin, Yu.: Adaptive Methods of Near-Term Time Series Forecasting, p. 416. Finance and Statistics, Moscow (2003)

    Google Scholar 

  27. Snytyuk, V.E.: Forecasting Models Methods Algorithms, p. 364. Maklaut, Kyiv (2008)

    Google Scholar 

  28. Mulesa, O., Geche, F., Batyuk, A., Buchok, V.: Development of combined information technology for time series prediction. In: Shakhovska, N., Stepashko, V. (eds.) CSIT 2017. AISC, vol. 689, pp. 361–373. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70581-1_26

    Chapter  Google Scholar 

  29. Mulesa, O., Geche, F., Batyuk, A.: Information technology for determining structure of social group based on fuzzy c-means. In: 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT), pp. 60–62 (2015). https://doi.org/10.1109/stc-csit.2015.7325431

  30. Mulesa, O., Geche, F.: Designing fuzzy expert methods of numeric evaluation of an object for the problems of forecasting. Eastern-Eur. J. Enterpr. Technol. 3(4(81)), 37–43 (2016). https://doi.org/10.15587/1729-4061.2016.70515

  31. Morozov, V., Kalnichenko, O., Liubyma, I.: Managing projects configuration in development distributed information systems. In: 2017 2nd IEEE International Conference on Advances Information and Communication Technologies (AICT), pp. 154–157 (2017). https://doi.org/10.1109/aiact.2017.8020088

  32. Kuchansky, A., Biloshchytskyi, A., Andrashko, Yu., Biloshchytska, S., Shabala, Ye., Myronov, O.: Development of adaptive combined models for predicting time series based on similarity identification. Eastern-Eur. J. Enterpr. Technol. 1(4(91)), 32–42 (2018). https://doi.org/10.15587/1729-4061.2018.121620

  33. Kuchansky, A., et al.: Combined models for forecasting the air pollution level in infocommunication systems for the environment state monitoring. In: 2018 IEEE International Conferences on Intelligent Data Acquisition and Advanced Computing Systems, pp. 125–130 (2018). https://doi.org/10.1109/idaacs-sws.2018.8525608

  34. Hurst, H.E.: Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 116, 770–799 (1951)

    Google Scholar 

  35. Mandelbrot, B.B.: When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale. Models Rev. Econ. Stat. 53(3), 225–236 (1971)

    Article  MathSciNet  Google Scholar 

  36. Anis, A., Lloyd, E.: The expected value of the adjusted rescaled Hurst Range of independent normal summands. Biometrika 63, 111–116 (1976)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Taras Shevchenko National University of Kyiv, Kiev, Ukraine

    A. Kuchansky, A. Biloshchytskyi & S. Bronin

  2. Kyiv National University of Construction and Architecture, Kiev, Ukraine

    S. Biloshchytska

  3. State University “Uzhhorod National University”, Uzhhorod, Ukraine

    Yu. Andrashko

Authors
  1. A. Kuchansky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Biloshchytskyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Bronin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Biloshchytska
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu. Andrashko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Bronin .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Thrasyvoulos Tsiatsos

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kuchansky, A., Biloshchytskyi, A., Bronin, S., Biloshchytska, S., Andrashko, Y. (2021). Use of the Fractal Analysis of Non-stationary Time Series in Mobile Foreign Exchange Trading for M-Learning. In: Auer, M.E., Tsiatsos, T. (eds) Internet of Things, Infrastructures and Mobile Applications. IMCL 2019. Advances in Intelligent Systems and Computing, vol 1192. Springer, Cham. https://doi.org/10.1007/978-3-030-49932-7_88

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49932-7_88

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49931-0

  • Online ISBN: 978-3-030-49932-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation