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Retail Sales Forecasting Using Deep Learning: Systematic Literature Review

Linda Eglite, Institute of Applied Computer Systems, Riga Technical University, Meza 1 k1, Riga, LV-1048, Latvia, Ilze Birzniece, Institute of Applied Computer Systems, Riga Technical University, Meza 1 k1, Riga, LV-1048, Latvia
2022 Complex Systems Informatics and Modeling Quarterly  
This systematic literature review examines the deep learning (DL) models for retail sales forecast. The accuracy of a retail sales forecast is a prevalent force for uninterrupted business operations.  ...  The study analyses the DL frameworks used in reviewed literature. Tested DL models are listed, as well as other machine learning and linear models used for the evaluation comparison.  ...  Grocery, e-commerce, apparel and accessory industry, alcohol, health and beauty, and shopping mall sales have been forecasted by the authors.  ... 
doi:10.7250/csimq.2022-30.03 fatcat:yf6rr7veyjgwtad52wfj2unc7m

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology [article]

Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Quoc Tran, Brian Seaman
2019 arXiv   pre-print
Generating accurate and reliable sales forecasts is crucial in the E-commerce business.  ...  Aside from the forecasting framework, we also propose a systematic pre-processing framework to overcome the challenges in the E-commerce business.  ...  Introduction Generating product-level demand forecasts is a crucial factor in E-commerce platforms.  ... 
arXiv:1901.04028v2 fatcat:s7l4cpsi7rcz7dv6o7co24k4fe

Sales Forecast in E-commerce using Convolutional Neural Network [article]

Kui Zhao, Can Wang
2017 arXiv   pre-print
Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions.  ...  Sales forecast is a challenging problem in that sales is affected by many factors including promotion activities, price changes, and user preferences etc.  ...  Inaccurate forecasts may lead to stockout or overstock, hurting the decision efficiency in E-commerce.  ... 
arXiv:1708.07946v1 fatcat:67op24vmk5eilacdetdmykq2cq

Extreme Gradient Boosting Model-based Forecasting of Big Data Online Sales Record

Gagan Sharma, Sunil Patil
2022 SAMRIDDHI A Journal of Physical Sciences Engineering and Technology  
Nowadays, big data plays a crucial role for many online e-commerce businesses to generate more sales.  ...  PySpark, as the best suitable and compatible framework, is used for data analysis.  ...  [20] proposed Bayesian learning based on a neural network to forecast sales rates using retailers' big data.  ... 
doi:10.18090/samriddhi.v14i01.18 fatcat:qdlkfyuwv5f3vbsxx3b3hizxq4

Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

Xiaoting Yin, Xiaosha Tao, Rahman Ali
2021 Scientific Programming  
constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products.  ...  In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.  ...  a neural network prediction model for cigarette sales by using the improved BP neural network Levenberg-Marquardt algorithm [2] .  ... 
doi:10.1155/2021/2179692 fatcat:umcj4ha3sfhc3eawettf4cynv4

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

Juntao Li, Tianxu Cui, Kaiwen Yang, Ruiping Yuan, Liyan He, Mengtao Li
2021 Sustainability  
Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises.  ...  To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce  ...  Design of E-Commerce Enterprise Demand Forecasting Method Based on HF-ConvLSTM The typical HF-ConvLSTM-based e-commerce enterprise demand forecasting method framework is depicted in Figure 5, including  ... 
doi:10.3390/su132313050 fatcat:ilecarg5efdnlnrlncak26rfzi

Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

Jawaria Sallar, Sallar Khan, Shariq Ahmed, Parshan Kumar, Hasham Faridy, Mahaveer Rathi
2021 Revista GEINTEC  
In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according  ...  For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather.  ...  Sallar Khan for his continuous support in the paradigm of research implementation methods.  ... 
doi:10.47059/revistageintec.v11i4.2318 fatcat:ksqyhs346zcndmlcjv5nskozy4

Semantic Web Mining in Retail Management System using ANN

2019 International journal of recent technology and engineering  
To satisfy the customers' requirements knowing the consumer behaviour and interests are more important in the e-commerce environment.  ...  Based on these considerations, this paper gives detail review about a Semantic web mining based Artificial Neural Network (ANN) for the retail management system.  ...  The primary purpose of the framework was the customer assessment in ranking E-commerce websites and easy searching as well as a perfect ranking of E-Commerce websites.  ... 
doi:10.35940/ijrte.b1439.0982s1119 fatcat:4tyetp4ve5gdfdr5cp6mccw3su

Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce [article]

Borui Ye, Shuo Yang, Binbin Hu, Zhiqiang Zhang, Youqiang He, Kai Huang, Jun Zhou, Yanming Fang
2022 arXiv   pre-print
E-commerce has gone a long way in empowering merchants through the internet.  ...  In this article, we present a solution to better forecast GMV inside Alipay app.  ...  CONCLUSION In this paper, we propose Gaia, a novel GMV forecasting framework for e-sellers, to address the temporal deficiency and temporal shift issue.  ... 
arXiv:2207.13329v1 fatcat:phinjvccfbb3xlrrdze6lxktkm

Forecasting Models of Natural Gas

Meenakshi Thalor, Ritesh Choudhary, Ajay Jangid, Deep Gandhecha, Rishab Bhat
2021 International Journal of Scientific Research in Science and Technology  
Some of these difficulties can be eliminated by creating an online collaborative environment, which is setup free, provides a visual framework, and helps in understanding and implementing the basic and  ...  In this project, we are trying to create an online collaborative environment named "Visual Prediction", which is an online application that promotes visual based learning and provides a GUI based ML framework  ...  These technologies have proved to be beneficial to the society in various fields such as education, industries, e-commerce, etc.  ... 
doi:10.32628/ijsrst2182121 fatcat:u24zvxacsjgmpjxzfl5fb4edrm

Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach [article]

Junhao Hua, Ling Yan, Huan Xu, Cheng Yang
2021 arXiv   pre-print
Experimental results show the advantages of our pricing algorithm, and the proposed framework has been successfully deployed to the well-known e-commerce fresh retail scenario - Freshippo.  ...  In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period  ...  CONCLUSION In this paper, we present a novel pricing framework for markdowns in e-commerce fresh retails, consisting of counterfactual prediction and multi-period price optimization.  ... 
arXiv:2105.08313v2 fatcat:atxzob6c7veb5a6725o7h3lt54

Research on Catering Business Demand Forecasting Model Based on the Weather Sensitivity Theory and GBDT Algorithm

Jianming Dong, Ziyi Li, Xueci Chen, Fan Yang, Tianyu Gao, Jie Jin
2019 DEStech Transactions on Engineering and Technology Research  
In this paper, GBDT machine learning is introduced into the demand forecasting of catering e-commerce, and a weather-sensitive demand forecasting model based on GBDT is constructed.  ...  Finally, the experimental results show that the demand forecasting model based on GBDT has the highest accuracy and is an effective solution to solve the high demand fluctuation of catering e-commerce  ...  of a class of products of catering e-commerce.  ... 
doi:10.12783/dtetr/eeec2018/26856 fatcat:piiwit2ckrbr5dj6nzwzu744rm

Research on Product Sales Forecast Based on Test Sales Comment Data

Li Li, Hao Li, Jiamei Yang
2022 Journal of Education, Humanities and Social Sciences  
This study uses the BERT model to judge the sentiment tendency of reviews and identify product attributes, build a sales forecast model based on the extracted review features and the sales and product  ...  Among the prediction models constructed in the study, the multiple linear regression model has the worst effect, the prediction effect of random forest and BP neural network is close, and the prediction  ...  Product feature extraction and comment sentiment analysis The programming language used in this experiment is python, and the deep learning framework is Tensorflow.  ... 
doi:10.54097/ehss.v2i.781 fatcat:wrrcoyuyzvhzvfzctyvjeghc7m

Intermittent Demand Forecasting with Deep Renewal Processes [article]

Ali Caner Turkmen, Yuyang Wang, Tim Januschowski
2019 arXiv   pre-print
In this paper, we first make the connections between renewal processes, and a collection of current models used for intermittent demand forecasting.  ...  We then develop a set of models that benefit from recurrent neural networks to parameterize conditional interdemand time and size distributions, building on the latest paradigm in "deep" temporal point  ...  Here, intuitively, discretization error plays a larger role. Moreover, in more demand forecasting scenarios, exact timestamps are available for individual purchase eventssuch as in e-commerce.  ... 
arXiv:1911.10416v1 fatcat:j6u5fhghrjdine22qycigbvtyq

An improved deep forest model for prediction of e-commerce consumers' repurchase behavior

Weiwei Zhang, Mingyan Wang, Baogui Xin
2021 PLoS ONE  
Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics.  ...  This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce  ...  KNN is one of the simplest classification methods, which is widely used in vehicle sales forecast [18] , health monitoring [19] , housing price forecast [20] , and other fields.  ... 
doi:10.1371/journal.pone.0255906 pmid:34543319 pmcid:PMC8452008 fatcat:y7rgx6fe4ba6fpctcweg24ms7q
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