Preview

Economics of Contemporary Russia

Advanced search

Capacity Method of Rare Events Analysis in the Area of Services

https://doi.org/10.33293/1609-1442-2020-3(90)-132-142

Abstract

Imagine that you are owner of some service. You need to determine for a certain future period the work plan of your craftsmen, the number of consumables needed. To do this, you need to make a forecast of future services number. Classical mathematical methods of working with time series are not suitable for this task. Aggregation of data on services by months and the compilation of a time series can only confuse. Forecasting services should be performed using methods designed to work with rare events. Rare events are devoted to relatively few works. Methods for the study of rare events are significantly less than methods for analyzing frequent events (time series). The most popular method of studying rare events at the moment is the use of the theory of random processes, which uses a stream of Poisson or Erlang events. However, using random streams, one cannot predict the very moment of the occurrence of an event. The paper describes an approach to the rare events analysis, which is based on: dividing events by identifiers of the sources in which they are formed; regression process parameters occurring within the sources, resulting in these events formation; search by any known method of parameters change patterns; the process start itself to obtain a forecast of the following events time occurrence. For the consumption processes and the disturbances growth process, which are the most common processes of the events formation in the economy, a method is proposed for restoring the consumption or accumulating disturbances rate from the rare events history. Services as can be modeled as the process of accumulating disturbances to a certain level. The article is devoted to the application of the capacity method of rare events analysis on real data in the service sector (haircut in a hairdresser, a manicure in a beauty salon, cellular communication services). The task is to restore the function that leads to the acquisition of services, and then predict the following events.

About the Authors

Yuriy Aleksandrovich Korablev
Financial University under the Government of the Russian Federation, Moscow
Russian Federation


Polina Sergeevna Golovanova
Financial University under the Government of the Russian Federation, Moscow
Russian Federation


Tatyana Andreevna Kostritsa
Financial University under the Government of the Russian Federation, Moscow
Russian Federation


References

1. Dzanagova I. T., Khugaeva L. T. (2015). Information-statistical methods for constructing extremal models of rare events. Fundamental Research. Academy of Natural History (Penza), part 6, no. 11, pp. 1081–1084 (in Russian).

2. Kislyakov A. N. (2019). Method of virtual increase in the sample when predicting rare sales in conditions of information asymmetry. Vestnik of Altai Academy of Economics and Law, vol. 2, no. 1, pp. 47–54 (in Russian).

3. Korablev Yu.A. (2017). Capacity method for analyzing rare sales in Excel. Ekonomika i Upravlenie: Problemy I Resheniya, vol. 3 (66), no. 6, pp. 224–230 (in Russian).

4. Korablev Yu.A. (2019). Error of the capacity method of rare events analysis, remoteness from the end user. The News of K.-B. Sc. Center of RAS, no. 3 (89), pp. 48–77 (in Russian). DOI: 10.35330/1991-6639-2019-3-89-48-77.

5. Korablev Yu.A. (2020). The function restoration method by integrals for analysis and forecasting of rare events in the economy. Economics and Mathematical Methods, vol. 56, no. 3, pp. 114–125 (in Russian).

6. Lukinsky V., Zamaletdinova D. (2015). Methods of inventory management: the calculation of inventory indicators for product groups related to rare events (Part I). Logistiсs, no. 1 (98), pp. 28–33 (in Russian).

7. Lukinsky V., Zamaletdinova D. (2015). Methods of inventory management: the calculation of inventory indicators for product groups related to rare events (Part II). Logistics, no. 2 (99), pp. 24–27 (in Russian).

8. Taleb N. N. (2007). The Black Swan: The Impact of the Highly Improbable. N.Y., The New York Times.

9. Shannon C. (1963). Works on information theory and cybernetics. Moscow (IL): Science and Education. Moscow, Inostrannaya literatura (in Russian). URL: http://www.sciepub.com/reference/5832

10. Claude E. (1949). Shannon and Warren Weaver: The Mathematical Theory of Communication. Urbana: The University of Illinois Press.

11. Cover T., Hart P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory. IEEE Transactions on Information Theory, vol. 13 (1), pp. 21–27.

12. Croston J. D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly (1970–1977), vol. 23 (3), pp. 289–303.

13. Quinn B. G., Fernandes J. M. (1991). A Fast Efficient Technique for the Estimation of Frequency. Biometrika, vol. 78, no. 3, September, pp. 489–497.

14. Quinn B. G., Hannan E. J. (2001). The Estimation and Tracking of Frequency. Cambridge: Cambridge University Press. 278 p.

15. Willemain T. R., Park D. S., Kim Y. B., Shin K. I. (2001). Simulation output analysis using the threshold bootstrap. European Journal of Operational Research, vol. 134 (1). pp. 17–28.


Review

For citations:


Korablev Yu.A., Golovanova P.S., Kostritsa T.A. Capacity Method of Rare Events Analysis in the Area of Services. Economics of Contemporary Russia. 2020;(3):132-142. (In Russ.) https://doi.org/10.33293/1609-1442-2020-3(90)-132-142

Views: 786


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1609-1442 (Print)
ISSN 2618-8996 (Online)