A model for forecasting the volume of the entertainment services market under non-determinative conditions
DOI:
https://doi.org/10.30837/ITSSI.2023.25.129Keywords:
autoregression; film industry; subscription pricing model; forecasting; entertainment market.Abstract
The subject matter of the article is the theoretical-methodical and applied principles of modelling and forecasting indicators of the market volume of entertainment services. The goal of the work is to find a mechanism that will allow determining the volume of subscribers, taking into account exogenous variables, especially during socially unstable situations, such as a pandemic, war, cataclysms, etc. The following tasks are solved in the article: formation of criteria for companies for which the created mechanism is planned to be applied; review of basic autoregression models; determination of factors that should serve as an external influence when predicting the number of subscribers; creation of a multi-criteria choice problem; conducting experiments according to the proposed methodology to test the proposed hypotheses and systematize the obtained results. The following methods are used: analytical and inductive methods for forming factors of external influence and description of target companies; expert evaluation method for determining the most influential external indicators; experimental method, statistical methods of processing time series and methods of multi-criteria evaluation to determine the most effective autoregression model. The following results were obtained: the factors of external influence were formed: As external variables, it was decided to choose: the dynamics of the incidence of coronavirus, the rate of change in the global gross domestic product, the change in the S&P500 index, and the news from the world's largest news agencies converted into numerical form; a set of criteria for comparing models was created, saving forecasting time, accuracy, the possibility of taking into account external influence and the specificity of taking it into account; it was determined that the most accurate model is autoregression of the moving average, which at the same time is the most effective model given the created problem of multi-criteria selection; the similarity of the obtained results of experiments with global and domestic research is established. Conclusions: the use of analytical and inductive methods in combination with an experimental approach made it possible to obtain an effective (with an accuracy of more than 95%) mechanism for forecasting the market volume of companies that operate in the film industry and have a signature pricing model. The obtained result will allow players with a smaller market volume not to lose their audience due to the instability of the external environment, and, accordingly, will stimulate the development of the industry in general.
References
Список літератури
Bagnoli С., Biazzo S., Biotto G. Digital business models for Industry 4.0. How innovation and technology shape the future of companies. Springer, Cham. 2022. 268 р. DOI: 10.1007/978-3-030-97284-4
Oyewola D. O., Dada E. G. Machine Learning Methods for Predicting the Popularity of Movies. Journal of Artificial Intelligence and Systems. 2020. № 4. Р. 65–82. DOI: 10.33969/AIS.2022040105
Wang W., Guo Q. Subscription strategy choices of network video platforms in the presence of social influence. Electronic Commerce Research. 2021. № 23, Р. 577–604. DOI: 10.1007/s10660-021-09504-w
Kerschbaumer R. H., Foscht T., Eisingerich A. B. Smart Services and the Rise of Access-based Subscription Models. Smart Services, Wiesbaden: Springer Gabler, 2022. Р. 179–205. DOI: 10.1007/978-3-658-37346-7_6
Shin Z., Moon J., Rho S. A Comparative Analysis of Ensemble Learning-Based Classification Models for Explainable Term Deposit Subscription Forecasting. Journal of Society for e-Business Studies. Vol. 3. № 26. Р. 1–21. URL: http://www.jsebs.org/jsebs/index.php/jsebs/article/view/457 (дата звернення: 08.09.2023).
Choujun Zhan; Jianbin Li; Wei Jiang; Wei Sha; Yijing Guo E-commerce Sales Forecast Based on Ensemble Learning. IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-CN). 2020. Р. 1–5. DOI: 10.1109/ISPCE-CN51288.2020.9321858
Masini R. P., Medeiros M. C., Mendes E. F. Machine learning advances for time series forecasting. Journal of Economic Surveys. 2021. Vol. 37. № 1. Р. 76–111. DOI: 10.1111/joes.12429
Ullah I., Raza B., Malik A. K., Imran M., Islam S. U., & Kim S. W. A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector IEEE Access. 2019. № 7. Р. 60134–60149. DOI: 10.1109/access.2019.2914999
Ning C., You F. Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Computers & Chemical Engineering. 2019. № 125. Р. 434–448. DOI: 10.1016/j.compchemeng.2019.03.034
Qi X.-Z., Ning Z., Qin M. Economic policy uncertainty, investor sentiment and financial stability–an empirical study based on the time varying parameter-vector autoregression model. Journal of Economic Interaction and Coordination. 2022. № 17. Р. 779–799. DOI: 10.1007/s11403-021-00342-5
Shibasaki M., Witayangkurn A., & Rahman M. M. Comparison of life patterns from mobile data in Bangladesh. Smart Technology & Urban Development (STUD-2019): 1st International Conference, Chiang Mai, 13 December – 14 December 2019: IEEE, 2019. P. 1–7. DOI: 10.1109/STUD49732.2019.9018795
Khovrat A., Kobziev V., Nazarov A., & Yakovlev S. Parallelization of the VAR Algorithm Family to Increase the Efficiency of Forecasting Market Indicators During Social Disaster. Information Technology and Implementation (IT&I 2022): 9th Internaional Conference, Kyiv, 30 November – 2 December 2022: CEUR Workshop Proceedings. No. 3347, P. 222–233. URL: https://ceur-ws.org/Vol-3347/Paper_19.pdf (дата звернення: 08.09.2023).
Wang W., Guo Q. Subscription strategy choices of network video platforms in the presence of social influence. Electronic Commerce Research. 2021. № 23. Р. 577–604. DOI:10.54691/bcpbm.v34i.3018
Haslbeck J., Bringmann L., Waldorp L. A Tutorial on Estimating Time-Varying Vector Autoregressive Models. Multivariate Behavioral Research. 2021. Vol. 56. № 1. Р. 120–149. DOI: 10.1080/00273171.2020.1743630
Afanasieva I., Golian N., Golian V., Khovrat A., & Onyshchenko K. Application of Neural Networks to Identify of Fake News. Computational Linguistics and Intelligent Systems (COLINS 2023): 7th International Conference, Kharkiv, 20 April – 21 April 2023: CEUR workshop proceedings, No. 3396, 2023. P. 346–358. URL: https://ceur-ws.org/Vol-3396/paper28.pdf (дата звернення: 08.09.2023).
References
Bagnoli, С., Biazzo, S., Biotto, G. (2022), "Digital business models for Industry 4.0. How innovation and technology shape the future of companies". Springer, Cham. 268 р. DOI: 10.1007/978-3-030-97284-4
Oyewola, D. O., Dada, E. G. (2022), "Machine Learning Methods for Predicting the Popularity of Movies", Journal of Artificial Intelligence and Systems, No. 4, P. 65–82. DOI: 10.33969/AIS.2022040105
Wang, W., & Guo, Q. (2021), "Subscription strategy choices of network video platforms in the presence of social influence", Electronic Commerce Research, No. 23, P. 577–604. DOI: 10.1007/s10660-021-09504-w
Kerschbaumer, R. H., Foscht, T., & Eisingerich, A. B. (2022), "Smart Services and the Rise of Access-based Subscription Models", In: Smart Services, Springer Gabler, Wiesbaden, P. 179–205. DOI: 10.1007/978-3-658-37346-7_6
Shin, Z., Moon, J., & Rho, S. (2021), "A Comparative Analysis of Ensemble Learning-Based Classification Models for Explainable Term Deposit Subscription Forecasting", Journal of Society for e-Business Studies, No. 26(3), P. 1–21, available at: http://www.jsebs.org/jsebs/index.php/jsebs/article/view/457 (last accessed 08.09.2023).
Li, J., Zhan, C., Sha, W., Jiang, W., & Guo, Y. (2020), "E-commerce Sales Forecast Based on Ensemble Learning". IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-CN). P. 1–5. DOI: 10.1109/ISPCE-CN51288.2020.9321858
Masini, R., Medeiros, M., Mendes, E. (2021), "Machine learning advances for time series forecasting", Journal of Economic Surveys, No. 37(1), P. 76–111. DOI: 10.1111/joes.12429
Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019), "A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector", IEEE Access, No. 7. P. 60134–60149. DOI: 10.1109/ACCESS.2019.2914999
Ning, C., & You, F. (2019), "Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming", Computers & Chemical Engineering, No. 125, P. 434–448. DOI: 10.1016/j.compchemeng.2019.03.034
Qi, X.-Z., Ning, Z., & Qin, M. (2022), "Economic policy uncertainty, investor sentiment and financial stability – an empirical study based on the time varying parameter-vector autoregression model". Journal of Economic Interaction and Coordination. No. 17. P. 779–799. DOI: 10.1007/s11403-021-00342-5
Shibasaki, M., Witayangkurn, A., & Rahman, M. M. (2019), "Comparison of life patterns from mobile data in Bangladesh". Smart Technology & Urban Development (STUD-2019): 1st International Conference, Chiang Mai, 13 December – 14 December 2019: IEEE, P. 1–7. DOI: 10.1109/STUD49732.2019.9018795
Khovrat, A., Kobziev, V., Nazarov, A., & Yakovlev, S. (2022), "Parallelization of the VAR Algorithm Family to Increase the Efficiency of Forecasting Market Indicators During Social Disaster". Information Technology and Implementation (IT&I 2022): 9th Internaional Conference, Kyiv, 30 November – 2 December 2022: CEUR Workshop Proceedings. No. 3347, P. 222–233. available at: https://ceur-ws.org/Vol-3347/Paper_19.pdf (last accessed: 08.09.2023).
Wang, G., Wang, Zh., & Xie, Y. (2022), "Subscribers forecasting of netflix based on multiple linear models", BCP Business & Management, No. 34, P. 229–236. DOI:10.54691/bcpbm.v34i.3018
Haslbeck, J., Bringmann, L., & Waldorp, L. (2021), "A Tutorial on Estimating Time-Varying Vector Autoregressive Models". Multivariate Behavioral Research, No. 56 (1), P. 120–149. DOI: 10.1080/00273171.2020.1743630
Afanasieva, I., Golian, N., Golian, V., Khovrat, A., & Onyshchenko, K. (2023), "Application of Neural Networks to Identify of Fake News". Computational Linguistics and Intelligent Systems (COLINS 2023): 7th International Conference, Kharkiv, 20 April – 21 April 2023: CEUR workshop proceedings, No. 3396, P. 346–358. available at: https://ceur-ws.org/Vol-3396/paper28.pdf (last accessed: 08.09.2023).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
