Proceedings of International Conference on Applied Innovation in IT
2024/03/07, Volume 12, Issue 1, pp.71-77

Comparative Analysis of Holt-Winters Algorithms on the Oracle Machine Learning Platform


Oleksandr Redych and Ruslan Boichuk


Abstract: The Oracle Cloud Machine Learning toolkit was used in the work to study time series forecasting methods. The choice of tools was dictated by conducting research at the State Tax University (Irpin) for further use of the results in the work of regional and central divisions of the state tax service, whose information systems are developed on the Oracle platform. Holt-Winters exponential smoothing algorithms, currently represented by the functions of the application programming interface for machine learning models of the PL/SQL package DBMS_DATA_MINING, were investigated. The research used personal income tax data for 2015-2023, obtained from the tax office in the Ivano-Frankivsk region. Higher accuracy (MAE=3.57%) was shown by the algorithm of the Holt-Winters multiplicative exponential smoothing model with a fading multiplicative trend and multiplicative seasonality. Oracle Machine Learning for SQL implements exponential smoothing using a state of the art state space method that incorporates a single source of error (SSOE) assumption which provides theoretical and performance advantages. Access to research results is organized using the APEX web application creation tool. The considered toolkit will help in making decisions when assessing the base of the tax potential of the region and planning tax revenues. The results of the Holt-Winters exponential smoothing model algorithms research are presented and the losses of personal income tax in Ivano-Frankivsk region in 2022 are estimated

Keywords: Machine Learning, Oracle Cloud, APEX, Holt-Winters, Personal Income Tax

DOI: 10.25673/115644; PPN 1884680062

Download: PDF

References:

  1. O. Puhachenko and T. Fomina, "Information support for expert studies and tax audits of the procedure for calculating and paying taxes and fees," Economics and society, no. 52, 2023, [Online]. Available: https://doi.org/10.32782/2524-0072/2023-52-72.
  2. T.V. Donchenko and A.V. Oliynyk, "Stages of formation of the tax system of Ukraine in the context of the development of the digital economy," Collection of scientific works of the State Fiscal Service University of Ukraine, no. 1, pp. 45-62, 2019, [Online]. Available: https://core.ac.uk/download/pdf/287302574.pdf.
  3. S.P. Kanzyuba, R.M. Matviychuk, Y.M. Sydorovych, and P.M. Musienko, "Electronic document flow. Reengineering of administrative processes in public authorities," Electronic governance and electronic democracy. Education manual, vol. 9, 64 p., 2017, [Online]. Available: https://books.google.de/books?id=paW4DwAAQBAJ&printsec=frontcover&hl=uk&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.
  4. O. Totska and I. Dmytruk, "Individual income tax in Ukraine: national and regional dimension," FCS, vol. 1, no. 8, pp. 30-39, Mar. 2023, [Online]. Available: https://doi.org/10.26565/2786-4995-2023-1-04.
  5. E.I. Maslennikov and A.O. Gusev, "Income tax of individuals in Ukraine: current state and ways of reform," Black Sea Economic Studies, no. 57, pp. 47-50, 2020, [Online]. Available: https://doi.org/10.32843/bses.57-8.
  6. Oracle, "Oracle Machine Learning for SQL Use Cases," 2023, [Online]. Available: https://docs.oracle.com/en/database/oracle/machine-learning/oml4sql/21/mlsql/machine-learning-functions.html#GUID-E5D374FB-F7D5-49ED-BBD4-272B8B8CADC2.
  7. A. Png and H. Helskyaho, "Oracle Machine Learning in Autonomous Database. In: Extending Oracle Application Express with Oracle Cloud Features," Apress, Berkeley, CA, pp.139-191, 2022, [Online]. Available: https://doi.org/10.1007/978-1-4842-8170-3_5.
  8. P.R. Winters, "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, vol. 6, no. 3, pp.324-342, 1960.
  9. C.E. Holt, "Forecasting Seasonals and Trends by Exponentially Weighted Averages," ONR Memorandum, no. 52, Carnegie Institute of Technology, Pittsburgh, USA, 1957. Available from the Engineering Library, University of Texas at Austin, 2003. Published in International Journal of Forecasting, vol. 20, pp. 1-13, 2004, with additional commentaries.
  10. C. Chatfield, "The Holt-Winters Forecasting Procedure," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 27, no. 3, pp. 264-279, 1978, [Online]. Available: https://doi.org/10.2307/2347162.
  11. J. K. Ord, et al., "Time Series Forecasting: The Case for the Single Source of Error State Space Approach," Working Paper, Department of Econometrics and Business Statistics, Monash University, VIC 3800, Australia, April 2005, [Online]. Available: https://robjhyndman.com/papers/SSOE.pdf.
  12. Oracle, "DBMS_DATA_MINING - Algorithm Settings: Exponential Smoothing," 2023, [Online]. Available: https://docs.oracle.com/en/database/oracle/oracle-database/21/arpls/DBMS_DATA_MINING.html#GUID-A95A0A38-8A5A-4470-B49F-80D81C588BFC.
  13. Oracle, "Oracle® APEX," 2023, [Online]. Available: https://docs.oracle.com/en/database/oracle/apex/23.1/htmrn/index.html#Oracle%C2%AE-APEX.
  14. L. Canali, Z. Baranowski, and P. Kothuri, "Integration of Oracle and Hadoop: Hybrid Databases Affordable at Scale," Journal of Physics: Conference Series, vol. 898(2), 2017, doi: 10.1088/1742-6596/898/4/042055.
  15. A. Png and H. Helskyaho, "Oracle Machine Learning REST Services. In: Extending Oracle Application Express with Oracle Cloud Features," Apress, Berkeley, CA, pp.193-232, 2022, [Online]. Available: https://doi.org/10.1007/978-1-4842-8170-3_6.

    HOME

       - Call for Papers
       - For authors
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 12, Issue 1 (ICAIIT 2024)        - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

DOI: http://dx.doi.org/10.25673/115729


        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.