Machine Learning Based Restaurant Sales Forecasting

Schmidt, Austin; Kabir, Md. Wasi Ul; Hoque, Tamjidul

doi:10.3390/make4010006

Cited by 33 publications

(11 citation statements)

References 48 publications

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“…A study leverages several machine learning (ML) models, including recurrent neural network (RNN) models, such as LSTM and Temporary Fusion Transformer, to present models for accurate sales forecasting for restaurants. The results of the study confirmed that the RNN model shows the highest performance when trends and seasonality are preserved [33]. A study utilizes RNN, LSTM, and GRU models for precise power consumption prediction in IoT and big data settings, revealing that the ensemble model combining the three models achieves the highest accuracy rate of 98.43% [34].…”

Section: Introductionmentioning

confidence: 54%

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Hwang,

Yoon,

Baek

et al. 2023

Electronics

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In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones. The main approach is to develop an integrated sales forecasting model by training the sales patterns and product characteristics of the same product category. In particular, we analyze the performance of the latest 12 machine learning models and propose the best performance model. Machine learning models have been used to compare performance through the development of Ridge, Lasso, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), AdaBoost, LightGBM, XGBoost, CatBoost, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). We apply a dataset consisting of monthly sales data of 38 mobile phones obtained in the Korean market. As a result, the Random Forest model was selected as an excellent model that outperforms other models in terms of prediction accuracy. Our model achieves remarkable results with a mean absolute percentage error (MAPE) of 42.6258, a root mean square error (RMSE) of 8443.3328, and a correlation coefficient of 0.8629.

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Section: Introductionmentioning

confidence: 54%

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Hwang,

Yoon,

Baek

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Forecasting future products sales enables stores and companies to avoid food waste. Therefore, [11] presented a case study of several ML models using real-time sales data from a restaurant. They applied data by using over 20 models to demonstrate the impact of creating stationary data-sets on the pre-processing of the feature and model training processes.…”

Section: Literature Review: Demand Forecasting Models In the Food Ind...mentioning

confidence: 99%

Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches

Nassibi¹,

Fasihuddin²,

Hsairi³

2023

IJACSA

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Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML's ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company's ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.

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“…In the context of sales forecast, the most commonly applied error measures are the RMSE [13,8,16,17,18], the MAPE [17,18,19] and MAE (Mean Absolute Error) [16,18,20]. The MAPE is easy to interpret but cannot be computed if the time series contains zeros [21].…”

Section: Statistical Sales Forecast Evaluationmentioning

confidence: 99%

A Dynamic Systems Model for an Economic Evaluation of Sales Forecasting Methods

Kuhlmann

Pauly

2023

Teh. glas. (Online)

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Sales forecasts are essential for a smooth workflow and cost optimization. Usually, they are assessed using statistical error measures, which might be misleading in a business context. This paper proposes a new dynamic systems model for an economic evaluation of sales forecasts. The model describes the development of the inventory level over time and derives the resulting overstock and shortage costs. It is tested on roughly 3,000 real-world time series and compared with the commonly used approach based on statistical measures. The experiments show that different statistical measures have no coherent evaluation, making their usage even less suitable for a practical economic application.

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Machine Learning Based Restaurant Sales Forecasting

Cited by 33 publications

References 48 publications

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches

A Dynamic Systems Model for an Economic Evaluation of Sales Forecasting Methods

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