Machine Learning based Restaurant Sales Forecasting

To encourage proper employee scheduling for managing crew load, restaurants need accurate sales forecasting. We collect real-world restaurant sales data to build a plethora of Machine Learning (ML) models to review their performances on such data for Sales forecasting. Two additional datasets are added to test methods of removing trend and seasonality by differencing and modeling training. Thus, we have collected three datasets and benchmarking this study to the best of our knowledge, and we are the first to collect restaurant sales data and review the performances of the plethora of ML models. To reduce forecasting error, we optimize the number of features per model through an exhaustive feature testing step. For the one-day forecasting, the best results are derived from the daily differenced dataset with simple linear models. Among these ML models, the RNN models perform comparably using the actual dataset, although they provide poor results on both others. On all accounts, the weekly differenced dataset performed poorly. When increasing to a one-week forecasting horizon, the temporal fusion transformer (TFT) model has unique advantages in one-week forecasting and achieves the best using the actual dataset showing results comparable to the best performing one-day forecast results. All other combinations of models and datasets performed worse than one-day forecasting when extending to full one-week forecasting.