A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

Mitra, Arnab; Jain, Arnav; Kishore, Avinash; Kumar, Pravin

doi:10.1007/s43069-022-00166-4

Cited by 37 publications

(22 citation statements)

References 76 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To forecast sales of US-based retail companies, a hybrid model of XGBoost, Random Forest (RF), and Linear Regression (LR) methodologies is proposed. The RF-XGBoost-LR model, an integrated model, performed better than the RF, Artificial Neural Network, gradient boosting, Adaboost, and XGBoost models [24].…”

Section: Introductionmentioning

confidence: 92%

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

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.

show abstract

Section: Introductionmentioning

confidence: 92%

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

“…1) Group 1: Consisting of five documents from the energy and retail sectors whose common characteristic is the use of "bagging" methods and data derived from the calendar such as holidays, weekends, weekdays, etc. The models proposed by these documents assemble the "bagging" methods with "boosting" methods, since the former are capable of compensating for the "overfitting" problems of the latter, while the latter correct the bias errors typical of the former [12], [13]. Other novel models from this group also propose the use of a "Generative adversarial network" to create "synthetic" data [14] and "transfer learning" [15], in both cases, to overcome the limited volume of data available for training.…”

Section: F Automatic Grouping Of Articlesmentioning

confidence: 99%

“…Complex and nonlinear data Fourteen of the 33 documents analyzed indicate that the main problem that the proposed "machine learning" models are intended to solve is the complexity and non-linearity of the patterns generated by the variables that affect the forecast. [12], [18], [20], [21], [22], [28], [23], [25], [26], [31], [43], [40], [41], [42].…”

Section: Keyword Inputmentioning

confidence: 99%

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Villar,

Lengua

2024

IJACSA

View full text Add to dashboard Cite

This systematic review, carried out under the PRISMA methodology, aims to identify the recently proposed artificial intelligence models for demand forecasting, distinguishing the problems they try to overcome, recognizing the artificial intelligence methods used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying what is new in them. Studies in the manufacturing, retail trade, tourism and electric energy sectors were considered in order to facilitate the transfer of knowledge from different sectors. 33 articles were analyzed, with the main results being that the proposed models are generally ensembles of various artificial intelligence methods; that the complexity of data and its scarcity are the main problems addressed; that combinations of simple machine learning, "bagging", "boosting" and deep neural networks, are the most used methods; that the performance of the proposed models surpasses the classic statistical methods and other reference models; and that, finally, the proposed novelties cover aspects such as the type of data used, the pattern extraction techniques used, the assembly forms of the applied models and the use of algorithms for automating the adjustment of the models. Finally, a forecast model is proposed that includes the most innovative aspects found in this research.

show abstract

“…Additionally, they demand extensive feature engineering and may suffer from over tting if not properly regularized [13,14,15].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Using Big Data to Analyze and Improve Emergency Department Efficiency: New Methods and Techniques

Abatal

Korchi

2023

Preprint

View full text Add to dashboard Cite

Efficiency is crucial, in Emergency Departments (EDs). It can be hindered by the number of patients. In this study we present a solution that utilizes the increasing amount of healthcare data and advancements in data analysis techniques. Our approach involves a combination of LSTM and Decision Tree models to enhance the accuracy of predicting volume, in EDs. The results indicate that our model outperforms existing methods suggesting its potential to improve ED efficiency.

show abstract

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

Cited by 37 publications

References 76 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

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Using Big Data to Analyze and Improve Emergency Department Efficiency: New Methods and Techniques

Contact Info

Product

Resources

About