A Comparative Study on Forecasting of Retail Sales

Hasan, Md Rashidul; Kabir, Muntasir A; Shuvro, Rezoan A; Das, Pankaz

doi:10.48550/arxiv.2203.06848

Cited by 3 publications

(4 citation statements)

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“…The performance of these models was analyzed and compared, with ARIMA showing the best results presenting a Root Mean Squared Error (RMSE) of 1.09. Although LightGBM had a higher RMSE of 1.18, it was more computationally efficient [Hasan et al 2022].…”

Section: Related Workmentioning

confidence: 98%

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Comparing Gradient Boosting Algorithms to Forecast Sales in Retail

Sousa,

do Rêgo,

Barbosa

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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The availability of data and the increased processing power of computers have made it easier to make decisions based on data, specially with Artificial Intelligence. One area where AI is widely applicable in companies is Supply Chain Management, particularly in demand forecasting. This paper aims to forecast sales for a company in the Cosmetic, Fragrance, and Toiletry market. Data from 2019 to 2023 were used from two different sales channel. To predict the demand, three Gradient Boosting algorithms (CatBoost, LightGBM, and XGBoost) were compared, and forecasts were made for three different time horizons (next period, five and ten periods ahead). After the experiments, LightGBM showed more stability compared to the other models.

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Section: Related Workmentioning

confidence: 98%

“…Nevertheless, there are also discrepancies. The studies conducted by [Hasan et al 2022] and [Spiliotis et al 2022] did not explore forecasts across various time horizons. [Li 2022] performed an experiment for distant time horizons of up to three months but did not focus on it.…”

Section: Related Workmentioning

confidence: 99%

Comparing Gradient Boosting Algorithms to Forecast Sales in Retail

Sousa,

do Rêgo,

Barbosa

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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show abstract

“…The study also indicated that the incorporation of additional explanatory variables can minimize forecasting errors. Similarly, GBM and LightGBM were assessed for their utility in forecasting future sales and promotions, demonstrating decent accuracy [16][17][18]. XGBoost, a widely used model in demand forecasting due to its strong performance in sales forecasting for retail, was found to be a favorable choice [19].…”

Section: Introductionmentioning

confidence: 99%

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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“…The article "A Comparative Study on Forecasting of Retail Sales" by M. Hasan, M.A. Kabir, R.A. Shuvro, and P. Das [6] addresses the challenging task of predicting product sales for large retail companies, acknowledging the complex nature of factors such as volatile trends, seasonalities, and unforeseen events like the COVID-19 outbreak. The paper offers a comprehensive theoretical overview and analysis of state-of-the-art timeseries forecasting models.…”

mentioning

confidence: 99%

The Application of Time-Series Forecasting Models in Grocery Retail Industry

Liashenko,

Yakymchuk

2023

tppe

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The grocery retail landscape in Ukraine has witnessed profound transformations, driven by disruptions like the COVID-19 pandemic and full-scale invasion, leading to unstable consumer behavior and market dynamics. In response, forecasting models must evolve to consider stochastic exogenous factors, such as blackout periods and air alarms. This study explores advanced time series forecasting models and proposes a comprehensive framework for optimal model selection. The study introduces the Neural Prophet, a model that combines interpretability and predictive power by incorporating components like non-periodic trends, periodic seasonality, holiday effects, and regressors. The research methodology involves a comparative analysis of classical time series forecasting methods, machine learning regression approaches, and neural networks. Noteworthy models include LightGBM, RNN, TCN, and Neural Hierarchical Interpolation for Time Series (N-HiTS). Optuna hyperparameter optimization and k-fold cross-validation enhance model accuracy. The study applies the proposed framework to forecast order quantities in the e-commerce segment of the Ukrainian grocery retail company. The system accommodates diverse factors like weather, holidays, and promotions, providing robust decision support. Anomalies are detected using the IQR method, and missing values are filled using Exponentially Weighted Moving Average. Results show the Neural Prophet consistently outperforming other models in 65% of cases, emphasizing its superiority. However, a complete transition to neural models results in reduced accuracy, highlighting the need for a nuanced approach based on data characteristics. The study presents a sophisticated framework for forecasting accuracy, supporting effective operational decision-making. Future research should explore ensemble methods while maintaining computational efficiency, aligning with the ongoing pursuit of optimized forecasting accuracy for informed decision-making in grocery retail.

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A Comparative Study on Forecasting of Retail Sales

Cited by 3 publications

References 0 publications

Comparing Gradient Boosting Algorithms to Forecast Sales in Retail

Comparing Gradient Boosting Algorithms to Forecast Sales in Retail

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

The Application of Time-Series Forecasting Models in Grocery Retail Industry

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