Welcome to Scholar Publishing Group

Machine Learning Theory and Practice, 2022, 3(4); doi: 10.38007/ML.2022.030407.

Machine Learning in the Prediction of Commodity Sales

Author(s)

Yuxi Zhou

Corresponding Author:
Yuxi Zhou
Affiliation(s)

Philippine Christian University, Philippine

Abstract

With the development of national economy, people's consumption level has also increased. In order to improve the utilization rate of products, reduce waste, and meet the requirements of environmental protection, it is necessary to improve the commodity sales forecast to reduce the waste of resources. Therefore, this paper proposes the role of machine learning algorithm in commodity sales forecasting. This paper mainly uses the cluster analysis method and the comparison method to carry on the experiment and the data analysis to the commodity sales volume forecast system. The experimental results show that the response time of the system designed in this paper is less than 2s, which can well meet the system requirements. Therefore, machine learning has played a greater role in the prediction of commodity sales.

Keywords

Machine Learning, Commodity Sales, Data Processing, Sales Forecast

Cite This Paper

Yuxi Zhou. Machine Learning in the Prediction of Commodity Sales. Machine Learning Theory and Practice (2022), Vol. 3, Issue 4: 53-60. https://doi.org/10.38007/ML.2022.030407.

References

[1] Seyed Ali Hasheminejad, Masoud Shabaab, Nahid Javadinarab: Developing Cluster-Based Adaptive Network Fuzzy Inference System Tuned by Particle Swarm Optimization to Forecast Annual Automotive Sales: A Case Study in Iran Market. Int. J. Fuzzy Syst. 24(6): 2719-2728 (2022). https://doi.org/10.1007/s40815-022-01263-6

[2] Xuan Bi, Gediminas Adomavicius, William Li, Annie Qu: Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness. INFORMS J. Comput. 34(3): 1644-1660 (2022). https://doi.org/10.1287/ijoc.2021.1147

[3] Marlene A. Smith, Murray J. Côté: Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer. INFORMS J. Appl. Anal. 52(4): 379-389 (2022). https://doi.org/10.1287/inte.2022.1119

[4] Kohei Takahashi, Yusuke Goto: Embedding-Based Potential Sales Forecasting of Bread Product. J. Adv. Comput. Intell. Intell. Informatics 26(2): 236-246 (2022). https://doi.org/10.20965/jaciii.2022.p0236

[5] Mert Girayhan Türkbayragí, Elif Dogu, Y. Esra Albayrak: Artificial Intelligence Based Prediction Models: Sales Forecasting Application in Automotive Aftermarket. J. Intell. Fuzzy Syst. 42(1): 213-225 (2022). https://doi.org/10.3233/JIFS-219187

[6] Austin Schmidt, Md Wasi Ul Kabir, Md. Tamjidul Hoque: Machine Learning Based Restaurant Sales Forecasting. Mach. Learn. Knowl. Extr. 4(1): 105-130 (2022). https://doi.org/10.3390/make4010006

[7] Shaohui Ma, Robert Fildes: Retail Sales Forecasting with Meta-Learning. Eur. J. Oper. Res. 288(1): 111-128 (2021). https://doi.org/10.1016/j.ejor.2020.05.038

[8] Carlos Aguilar-Palacios, Sergio Muñoz-Romero, José Luis Rojo-Álvarez: Cold-Start Promotional Sales Forecasting Through Gradient Boosted-Based Contrastive Explanations. IEEE Access 8: 137574-137586 (2020). https://doi.org/10.1109/ACCESS.2020.3012032

[9] Nadide Caglayan, Sule Itir Satoglu, E. Nisa Kapukaya: Sales Forecasting by Artificial Neural Networks for the Apparel Retail Chain Stores-An Application. J. Intell. Fuzzy Syst. 39(5): 6517-6528 (2020).

[10] Shakti Goel, Rahul Bajpai: Impact of Uncertainty in the Input Variables and Model Parameters on Predictions of a Long Short Term Memory (LSTM) Based Sales Forecasting Model. Mach. Learn. Knowl. Extr. 2(3): 256-270 (2020). https://doi.org/10.3390/make2030014

[11] Aldina Correia, Isabel Cristina Lopes, Eliana Costa e Silva, Magda Monteiro, Rui Borges Lopes: A Multi-Model Methodology for Forecasting Sales and Returns of Liquefied Petroleum Gas Cylinders. Neural Comput. Appl. 32(16): 12643-12669 (2020). https://doi.org/10.1007/s00521-020-04713-0

[12] Carlos Aguilar-Palacios, Sergio Muñoz-Romero, José Luis Rojo-Álvarez: Forecasting Promotional Sales within the Neighbourhood. IEEE Access 7: 74759-74775 (2019). https://doi.org/10.1109/ACCESS.2019.2920380

[13] Giuseppe Craparotta, Sébastien Thomassey, Amedeo Biolatti: A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data. Int. J. Comput. Intell. Syst. 12(2): 1537-1546 (2019). https://doi.org/10.2991/ijcis.d.191122.002

[14] Charu Gupta, Amita Jain, Nisheeth Joshi: DE-ForABSA: A Novel Approach to Forecast Automobiles Sales Using Aspect Based Sentiment Analysis and Differential Evolution. Int. J. Inf. Retr. Res. 9(1): 33-49 (2019). https://doi.org/10.4018/IJIRR.2019010103

[15] Abeer S. Desuky, Yomna M. Elbarawy, Samina Kausar, Asmaa Hekal Omar, Sadiq Hussain: Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem. IEEE Access 10: 11730-11749 (2022). https://doi.org/10.1109/ACCESS.2022.3146424

[16] Marwan H. Hassan, Saad M. Darwish, Saleh M. El-Kaffas: An Efficient Deadlock Handling Model Based on Neutrosophic Logic: Case Study on Real Time Healthcare Database Systems. IEEE Access 10: 76607-76621 (2022). https://doi.org/10.1109/ACCESS.2022.3192414

[17] Kanu Goel, Shalini Batra: Dynamically Adaptive and Diverse Dual Ensemble Learning Approach for Handling Concept Drift in Data Streams. Comput. Intell. 38(2): 463-505 (2022). https://doi.org/10.1111/coin.12475

[18] Arjun Puri, Manoj Kumar Gupta: Improved Hybrid Bag-Boost Ensemble with K-Means-SMOTE-ENN Technique for Handling Noisy Class Imbalanced Data. Comput. J. 65(1): 124-138 (2022). https://doi.org/10.1093/comjnl/bxab039