The Prediction of Douyin Live Sales based on Neural Network Algorithms

Jinman Sun; Xinling De; Hui Zheng

doi:10.28991/HIJ-2023-04-02-09

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Jinman Sun School of Economics and Management, Cangzhou Jiaotong College, Huanghua, Hebei 061199,, China
Xinling De
di92488@163.com
School of Economics and Management, Cangzhou Jiaotong College, Huanghua, Hebei 061199,, China
Hui Zheng School of Economics and Management, Cangzhou Jiaotong College, Huanghua, Hebei 061199,, China

Vol. 4 No. 2 (2023): June

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This paper aims to optimize neural network algorithms in order to improve their predictive performance and meet the demand for Douyin live sales forecasting. The Douyin live data of Florasis between January 2022 and June 2022 were collected from Huitun data and preprocessed. Using the Pearson correlation coefficient, six factors that are highly correlated with live sales were selected for subsequent prediction. This paper briefly introduces the back-propagation neural network (BPNN) algorithm and analyzes its parameter optimization methods, including particle swarm optimization (PSO), the artificial bee colony (ABC) algorithm, and the beetle antler search (BAS) algorithm. Then, an improved beetle antennae search (IBAS) algorithm was developed by introducing inertia weight and used to construct an IBAS-BPNN model for predicting sales volume in Douyin live streaming. The results showed that compared with the BPNN, PSO-BPNN, and ABC-BPNN algorithms, the IBAS-BPNN algorithm had better prediction performance, with a root-mean-square error of 335.6694, a mean absolute percentage error of 0.0532%, an equilibrium coefficient of 0.9889, and a shorter training time of 90.07 s. The experimental results demonstrate the reliability of the IBAS-BPNN algorithm for predicting sales volume in Douyin live streaming, providing new insights into parameter optimization of BPNN and offering references for further research on BPNN parameter optimization. It also provides an effective method with both timeliness and high accuracy for predicting sales volume in Douyin live streaming in practical applications.

Doi: 10.28991/HIJ-2023-04-02-09

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Peng, L., Lu, G., Pang, K., & Yao, Q. (2021). Optimal farmer's income from farm products sales on live streaming with random rewards: Case from China's rural revitalization strategy. Computers and Electronics in Agriculture, 189, 106403. doi:10.1016/j.compag.2021.106403.

Liu, K., Liu, B., Xu, H., He, Y., & Cao, Y. (2020). Research on e-commerce live broadcasts helping poverty alleviation under the influence of the COVID-19 : Take Xinhua County, Hunan Province as an example. 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). doi:10.1109/icemme51517.2020.00176.

Cheng, H., Huang, Y. T., & Huang, J. (2022). The Application of DEMATEL-ANP in Livestream E-Commerce Based on the Research of Consumers' Shopping Motivation. Scientific Programming, 2022, 1–15. doi:10.1155/2022/4487621.

Sharma, P., Khater, S., & Vashisht, V. (2021). Sales Forecast of Manufacturing Companies using Machine Learning navigating the Pandemic like COVID-19. 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM). doi:10.1109/iccakm50778.2021.9357751.

Boone, T., Ganeshan, R., Hicks, R. L., & Sanders, N. R. (2018). Can Google Trends Improve Your Sales Forecast? Production and Operations Management, 27(10), 1770–1774. doi:10.1111/poms.12839.

Moro, M. F., Weise, A. D., & Bornia, A. C. (2020). Model hybrid for sales forecast for the housing market of Sí£o Paulo. Real Estate Management and Valuation, 28(3), 45–64. doi:10.1515/remav-2020-0023.

Ma, J., Yu, J., & Gong, Z. (2019). EV Regional Market Sales Forecast Based On GABP Neural Network. IOP Conference Series: Earth and Environmental Science, 252, 032098. doi:10.1088/1755-1315/252/3/032098.

Wang, S., & Yang, Y. (2021). M-GAN-XGBOOST model for sales prediction and precision marketing strategy making of each product in online stores. Data Technologies and Applications, 55(5), 749–770. doi:10.1108/DTA-11-2020-0286.

Raiyani, A., Lathigara, A., & Mehta, H. (2021). Usage of time series forecasting model in Supply chain sales prediction. IOP Conference Series: Materials Science and Engineering, 1042(1), 012022. doi:10.1088/1757-899x/1042/1/012022.

Takahashi, K., & Goto, Y. (2022). Embedding-Based Potential Sales Forecasting of Bread Product. Journal of Advanced Computational Intelligence and Intelligent Informatics, 26(2), 236–246. doi:10.20965/jaciii.2022.p0236.

Zhan, M., Gao, H., Liu, H., Peng, Y., Lu, D., & Zhu, H. (2021). Identifying market structure to monitor product competition using a consumer-behavior-based intelligence model. Asia Pacific Journal of Marketing and Logistics, 33(1), 99–123. doi:10.1108/APJML-08-2019-0497.

Liu, Y., Yang, X., Zhu, C., & Meng, J. (2021). Drugs Sale Forecasting Based on SVR Integrated Promotion Factors. Journal of Physics: Conference Series, 1910(1), 012056. doi:10.1088/1742-6596/1910/1/012056.

Prakash, K. V., & Yogeswari, K. (2022). A Structural Equation Modelling (SEM) approach on Construction Noise Related Perception and Behavior among the Workforces. Civil Engineering Journal, 8(12), 2721-2733. doi:10.28991/CEJ-2022-08-12-04.

Kumar, P., & Singh, A. K. (2022). A comparison between MLR, MARS, SVR and RF techniques: hydrological time-series modeling. Journal of Human, Earth, and Future, 3(1), 90-98. doi:10.28991/HEF-2022-03-01-07.

Mendonça, Y. V., Naranjo, P. G. V., & Pinto, D. C. (2023). The Role of Technology in the Learning Process: A Decision Tree-Based Model Using Machine Learning. Emerging Science Journal, 6(Special Issue), 280-295. doi:10.28991/ESJ-2022-SIED-020.

Rusen, V., Krukonis, A., & Plonis, D. (2021). Prediction of Parameters of Semiconductor Band-pass Filters using Artificial Neural Network. 2020 IEEE 8th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). doi:10.1109/aieee51419.2021.9435748.

Asaad, R. R., & Ali, R. I. (2019). Back Propagation Neural Network (BPNN) and Sigmoid Activation Function in Multi-Layer Networks. Academic Journal of Nawroz University, 8(4), 216–221. doi:10.25007/ajnu.v8n4a464.

Wibawa, A. P., Izdihar, Z. N., Utama, A. B. P., Hernandez, L., & Haviluddin. (2021). Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). doi:10.1109/icaiic51459.2021.9415197.

Jin, D., Liu, Y., Ma, X., & Song, Q. (2021). Long time prediction of human lower limb movement based on IPSO-BPNN. Journal of Physics: Conference Series, 1865(4), 042099. doi:10.1088/1742-6596/1865/4/042099.

Rezaei, S., & Behnamian, J. (2022). Benders decomposition-based particle swarm optimization for competitive supply networks with a sustainable multi-agent platform and virtual alliances. Applied Soft Computing, 114, 1–32. doi:10.1016/j.asoc.2021.107985.

Singh, S., Singh, N. J., & Gupta, A. (2022). System sizing of hybrid solar-fuel cell battery energy system using artificial bee colony algorithm with predator effect. International Journal of Energy Research, 46(5), 5847–5863. doi:10.1002/er.7526.

Bai, J., Tian, M., & Li, J. (2022). Control System of Liquid Fertilizer Variable-Rate Fertilization Based on Beetle Antennae Search Algorithm. Processes, 10(2), 357. doi:10.3390/pr10020357.

Umbarkar, A. J., Moon, L. R., & Sheth, P. D. (2018). Comparative Study of CEC'2013 Problem Using Dual Population Genetic Algorithm. International Journal of Information Engineering and Electronic Business, 10(5), 40–45. doi:10.5815/ijieeb.2018.05.06.

Mahmoudabadi, A., Kanaani, M., & Ghazimahalleh, F. P. (2022). Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price. HighTech and Innovation Journal, 3(1), 45-55. doi:10.28991/HIJ-2022-03-01-05.

Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2022). Graph Neural Network for Robust Public Transit Demand Prediction. IEEE Transactions on Intelligent Transportation Systems, 23(5), 4086–4098. doi:10.1109/TITS.2020.3041234.

Cheng, Y., Wang, C., Wu, J., Zhu, H., & Lee, C. K. M. (2022). Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Applied Soft Computing, 118, 108507. doi:10.1016/j.asoc.2022.108507.

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The Prediction of Douyin Live Sales based on Neural Network Algorithms

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Most Cited Articles

Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks

Physicochemical and Microstructural Characterization of Klias Peat, Lumadan POFA, and GGBFS for Geopolymer Based Soil Stabilization

Seismic Upgradation of RC Beams Strengthened with Externally Bonded Spent Catalyst Based Ferrocement Laminates

Temporal Trends of Rainfall and Temperature over Two Sub-Divisions of Western Ghats

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