Subject The article addresses the changes in the revenue of fast food outlets. Objectives The aim is to develop and investigate models for forecasting the revenues of fast food outlets, considering the specifics of operations, changes in revenues during the week and on holidays. Methods We apply methods of statistical processing of research results and a regression analysis. We have built an auto-regressive model, a model that includes seasonal and trend components, and a trend based on grouped data. The model parameters are evaluated, using the least squares method. Results We use data for two years to build three regression models to predict the company revenue during non-holidays, evaluate errors and significance of equations. To forecast the amount of revenue during holidays, we developed 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 in the study of financial performance of fast food restaurants. Conclusions We suggest using different models to forecast revenues on holidays and non-holidays. Our experiments show that this approach enables to improve the forecast of revenues.
Keywords: forecasting, revenue, regression model, fast food outlet, outlying case
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