Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.119-124

Comparative Analysis of Methods of Forecasting the Consumer Price Index for Food Products (on the Example of the Altai Territory)


Stepan Mezhov, Maxim Krayushkin


Abstract: At the moment, there are no uniform universal methods for forecasting regional indicators of economic development in general and the consumer price index in particular. But depending on how accurate and reasonable the forecasts of the consumer price index will be, the budget of the region will be drawn up so correctly and the parameters of the forecast of socio-economic development, in the calculation of which this indicator is used, will be accurately predicted. The article presents a comparative analysis of methods for forecasting the consumer price index for food products. First, the most popular methods of forecasting the consumer price index were identified. Then models of time series, neural networks and decision trees were built, as well as retro-forecasts of the consumer price index for food products based on them. It is revealed that neural networks provide higher accuracy of forecasts compared to other models. The result of the work was the forecast of the consumer price index for food products in the Altai Territory for 2021 based on the constructed neural network model. The constructed neural network models can be used in relevant organizations to increase the accuracy of the forecast of this indicator. In addition, such an approach can be used as a basis for forecasting other indicators that characterize the socio-economic development of regions.

Keywords: Consumer Price Index, Time Series, Neural Network, Decision Tree, Error Back Propagation, Gradient Boosting, Forecast

DOI: 10.25673/76942

Download: PDF

References:

  1. V. A. Baykov and A.Y. Tarasova, “An integratedapproach to forecasting the inflation rate”, Bulletinof the Moscow University of Finance and Law, 2019,no. 7, p. 49-57.
  2. N.N. Karabutov, “Interrelation of indicesdetermining the level of inflation”, Economy, Taxes,Law, 2020, no. 10, pp. 40-47.
  3. A. A. Skrobotov and A.V. Tsarev, “Forecasting ofmacroeconomic indicators of the Russian economy”,Economic development of Russia, 2020, no. 18,pp. 45-55.
  4. S. G. Shulgin, “Selection of variables for instabilityanalysis and forecasting using gradient boostingmodels”, System monitoring of global and regionalrisks, 2018, no.8, pp. 115-153.
  5. E. V. Balatsky, N. A. Ekimova, and M. A. Yurevich,“Short-term forecasting of inflation based on markermodels”, Forecasting problems, 2019, no. 5, p.28-40.
  6. I. A. Vakhrushev, “Forecasting the trend dynamicsof the stock market based on macroeconomic factorsusing a diffuse index”, Scientific Journal of ITMOResearch University, The series «Economics andEnvironmental Management», 2020, no. 5,pp. 42-48.
  7. F. E. Huseynova, “Analysis of medium-term trendsin the dynamics of inflationary processes of theRussian economy”, Skif. Questions of studentscience, 2019, no. 7, p. 12-20.
  8. I. N. Dementieva, “Application of the index methodin studies of consumer sentiment of the population”,Economic and social changes, 2019, no. 1,pp. 153-173.
  9. E. B. Mitsek and S. A. Mitsek, “Analysis of factorsof dynamics of the main macroeconomic variables ofthe Russian Federation”, Questions of management,2020, no. 1, pp. 47-61.
  10. A. Yu. Yakimchuk, A. I. Teplenko, andM.N. Konyagina, “The influence of the key rate oninflation rates in modern Russia”, Bulletin of theAcademy of Knowledge, 2020, no. 37, pp. 490-497.
  11. I. N. Dubina, Mathematical and statistical methods inempirical socio-economic research: textbook,Moscow, Finance and Statistics, 2010, 415 p.
  12. Deductor - platform capabilities. Official website ofBaseGroup Labs, 2021, [Online]. Available:https://basegroup.ru/deductor/description.
  13. F. Wasserman, “Neurocomputer technology”,Theory and practice: a textbook, Moscow, 1992.
  14. S. A. Terekhov, Lectures on the theory andapplications of artificial neural networks: textbook, -Snezhinsk, 1998.
  15. Database of examples of solving specificmanagement tasks in the statistica system. Officialwebsite of Statsoft, 2021, [Online]. Available:http://statsoft.ru/solutions/ExamplesBase/tasks.

    Home

    PARTICIPATION

       - Committees
       - Proceedings


    PROCEEDINGS

       - 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 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 


           ISSN 2199-8876
           Copyright © 2013-2021 Leonid Mylnikov, © 2022 at Anhalt University of Applied Sciences. All rights reserved.