Digest Finance

Models to Forecast Revenue of Fast Food Restaurants

Vol. 23, Iss. 2, JUNE 2018

PDF  Article PDF Version

Received: 26 January 2018

Received in revised form: 5 February 2018

Accepted: 22 February 2018

Available online: 30 June 2018


JEL Classification: С22, С53

Pages: 212–220


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

ORCID id: not available

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

ORCID id: not available

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.

Keywords: forecasting, revenue, regression model, fast food restaurant, outlying case


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Vol. 23, Iss. 2
June 2018