Models to Forecast Revenue of Fast Food Restaurants

Журнал «Дайджест-Финансы»
т. 23, вып. 2, июнь 2018

Получена: 26.01.2018

Получена в доработанном виде: 05.02.2018

Одобрена: 22.02.2018

Доступна онлайн: 30.06.2018


Коды JEL: С22, С53

Страницы: 212–220


Gribanova E.B. Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russian Federation 

ORCID id: отсутствует
SPIN-код: отсутствует

Solomentseva E.S. Tomsk State University of Control Systems and Radioelectronics, Tomsk, Russian Federation 

ORCID id: отсутствует
SPIN-код: отсутствует

Importance The article addresses changes in revenue of fast food restaurants.
Objectives The research develops and investigates models for forecasting revenue of fast food restaurants, considering the specifics of operations, changes in revenue on week days and holidays.
Methods We apply methods for statistical processing of findings and a regression analysis. We have built an autoregressive model, seasonality- and trend-specific model and a trend based on grouped data. The model parameters are evaluated by the least squares method.
Results We use data for two years' time to build three regression models to predict corporate revenue during business days, evaluate errors and significance of equations. To forecast the amount of revenue during holidays, we devised an algorithm to select a group of data that corresponds to a certain day of the week based on the analysis of outlying cases. We also present a case study on forecasting the revenue on a holiday, using the developed algorithm. The results of the analysis may be useful to study financial performance of fast food restaurants.
Conclusions and Relevance We suggest using different models to forecast revenue on holidays and other days. Our experiments show that this approach contributes to more precise forecast of revenue.

Ключевые слова: forecasting, revenue, regression model, fast food restaurant, outlying case

Список литературы:

  1. Lyubushin N.P., Babicheva N.E. [Analyzing the approaches to assess and forecast sales revenue subject to a seasonal component]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2004, no. 6, pp. 6–16. URL: Link (In Russ.)
  2. Odiyako N.N., Golodnaya N.Yu. [The use of the additive and multiplicative models forecasting]. Ekonomika i predprinimatel'stvo = Journal of Economy and Entrepreneurship, 2013, no. 12-1, pp. 667–674. (In Russ.)
  3. Weatherford L.R., Kimes S.E. A Comparison of Forecasting Methods for Hotel Revenue Management. International Journal of Forecasting, 2003, vol. 19, iss. 3, pp. 401–415. URL: Link00011-0
  4. Mitsel' A.A., Telipenko E.V. [Assessing the impact of indicators of financial-economic activity of enterprise revenues from sales]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2011, no. 27, pp. 57–64. URL: Link (In Russ.)
  5. Trusova E.O., Baranova I.V., Kulagina N.A. [Assessment of influence of external factors on revenue of the organization by means of econometric model]. Sibirskaya finansovaya shkola = Siberian Financial School, 2017, no. 2, pp. 84–86. (In Russ.)
  6. Musienko S.O. [Financial analysis and forecasting of the results of small businesses performance based on regression model]. Aktual'nye problemy ekonomiki i prava = Actual Problems of Economics and Law, 2017, no. 1, pp. 18–33. (In Russ.)
  7. Hu C., Chen M., McCain S.-L.C. Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant. Journal of Travel & Tourism Marketing, 2004, vol. 16, iss. 2-3, pp. 79–98. URL: Link
  8. Timofeev V.S., Kolesnikova A.Yu. [Retail Sales Forecasting]. Ekonomika i matematicheskie metody = Economics and Mathematical Methods, 2009, no. 3, pp. 48–63. (In Russ.)
  9. Orlova I.V., Filonova E.S. [The Choice of Exogenous Factors in the Regression Model with Multicollinearity in the Data]. Mezhdunarodnyi zhurnal prikladnykh i fundamental'nykh issledovanii = International Journal of Applied and Fundamental Research, 2015, no. 5-1, pp. 108–116. (In Russ.)
  10. Grigor'eva S.V. [Econometric analysis of financial condition of road transport companies]. Voprosy ekonomiki i prava = Problems of Economics and Law, 2012, no. 6, pp. 135–138. (In Russ.)
  11. Noakk N.V., Nevolin I.V., Tatarnikov A.S. [Methods to forecast revenues from renting movies]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2012, no. 48, pp. 17–24. URL: Link (In Russ.)
  12. Ekmiş M.A., Hekimoğlu M., Atak Bülbül B. Revenue Forecasting Using a Feed-Forward Neural Network and ARIMA Model. Sigma Journal of Engineering and Natural Sciences, 2017, vol. 8, iss. 2, pp. 129–134.
  13. Terui N., Dijk H. Combined Forecasts from Linear and Nonlinear Time Series Models. International Journal of Forecasting, 2002, vol. 18, iss. 3, pp. 421–438. URL: Link00120-0
  14. Zhang G. Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 2003, vol. 50, pp. 159–175. URL: Link00702-0
  15. Gribanova E.B., Tugar-ool P.E. [The method for solving inverse problems of economic analysis using statistical data]. Korporativnye finansy, 2017, no. 3, pp. 111–120. (In Russ.) URL: Link
  16. Myasnikova E.N. [Formation of demand at restaurant business enterprises]. Nauchnyi vestnik MGIIT = Scientific Bulletin MSIIT, 2012, no. 4, pp. 26–31. (In Russ.)
  17. Mordovchenkov N.V., Sidyakova V.A. [Definition of quality of services on means of marketing researches of restaurants]. Azimut nauchnykh issledovanii: ekonomika i upravlenie = The Azimuth of Scientific Research: Economics and Management, 2015, no. 1, pp. 114–118. (In Russ.)
  18. Lasek A., Cercone N., Suanders J. Restaurant Sales and Customer Demand Forecasting: Literature Survey and Categorization of Methods. In: Smart City 360° – First EAI International Summit, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers. Springer International Publishing, 2016, pp. 479–491. URL: Link

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ISSN 2311-9438 (Online)
ISSN 2073-8005 (Print)

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т. 23, вып. 3, сентябрь 2018

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