Prophet Based Forecasting of Daily Tiktok Affiliate Revenue Using Operational Variables: The Cutetastic Case
DOI:
https://doi.org/10.35870/ijmsit.v6i2.7571Keywords:
Daily revenue, Extra regressors, Forecasting, Prophet, Social commerce, TikTok affiliateAbstract
This study evaluates the ability of the Prophet model to forecast daily TikTok affiliate revenue for the Cutetastic_ account and examines the contribution of operational variables to forecast accuracy. The dataset comprises 120 days of daily performance records from November 1, 2025, to February 28, 2026, including date, total revenue, number of video posts, video views, product clicks, and products sold. Three modeling scenarios were evaluated: univariate Prophet, Prophet with extra regressors, and Prophet with extra regressors combined with log transformation and parameter adjustment. The data were split chronologically into 106 training observations and 14 testing observations, and model performance was evaluated using MAE, RMSE, and MAPE. The results show that the univariate Prophet model produced a MAPE of 53.86%, whereas Prophet with extra regressors reduced the MAPE to 21.55%. The best performance was achieved by Prophet with extra regressors, log transformation, and parameter adjustment, with an MAE of 8,977,172.95, an RMSE of 11,549,223.21, and a MAPE of 19.31%. These findings indicate that historical revenue patterns alone are insufficient to capture the volatility of TikTok affiliate revenue. Operational variables improved forecast accuracy in this case study, particularly when treated as inputs for conditional forecasting or simulation-based forecasting. Therefore, the results from the regressor-based models should not be interpreted as assumption-free forecasts, but as short-term projections that depend on the availability or simulation of operational inputs.
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Albanna, H. T., & Diana, D. (2025). Forecasting Penjualan Sembako berbasis Model Prophet: Strategi Efisiensi Stok pada pada Ritel Tradisional. EDUMATIC Jurnal Pendidikan Informatika, 9(3), 698–707. https://doi.org/10.29408/edumatic.v9i3.32251
Alrawad, M., Laradi, S., Lutfi, A., Almaiah, M. A., & Alsharif, A. H. (2025). Investigating the factors influencing social commerce purchase intention: A hybrid study based on PLS-SEM and fsQCA. International Journal of Data and Network Science, 9(4), 1031–1050. https://doi.org/10.5267/j.ijdns.2024.9.016
Asiri, A. Y., Ghani, N. A., & Hamid, S. (2025). Examining the Predictors of Consumer Trust and Social Commerce Engagement: A Systematic Literature Review. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 247–247. https://doi.org/10.3390/jtaer20030247
Atamimi, F. M. H., Wintanti, W., & Abdillah, G. (2025). Enhanching Prophet Time Series Forecasting on Sparse Data via Hyperparameter Optimizattion: A Case Study in Retail. SinkrOn, 9(2), 1000–1007. https://doi.org/10.33395/sinkron.v9i2.14804
Avinash, A., Widjaja, A., & Karnalim, O. (2024). Analisis Perbandingan Algoritma Machine Learning untuk Forecasting Persediaan Produk Barang Pokok. Jurnal Teknik Informatika Dan Sistem Informasi, 10(2). https://doi.org/10.28932/jutisi.v10i2.9357
Budianto, P. A., Halim, E., Setiawan, A. N. A., & Effendi, H. R. (2025). Exploring the Impact of Evolving Consumer Behavior on Purchase Intention in Social Commerce Environments. Journal of Multimedia Trend and Technology., 4(2), 52–61. https://doi.org/10.35671/jmtt.v4i2.81
Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283–1318. https://doi.org/10.1016/j.ijforecast.2019.06.004
Ghazal, T. M., Kadhi, N. E., & Ahmad, M. (2025). Unlocking business insights with big data analytics and predictive AI: Discovering hidden patterns for accurate sales forecasting. International Journal of Advanced and Applied Sciences, 12(8), 237–245. https://doi.org/10.21833/ijaas.2025.08.022
Guen, V. L., & Thome, N. (2022). Deep Time Series Forecasting with Shape and Temporal Criteria. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 342–355. https://doi.org/10.1109/tpami.2022.3152862
Hajli, N., & Sims, J. (2015). Social commerce: The transfer of power from sellers to buyers. Technological Forecasting and Social Change, 94, 350–358. https://doi.org/10.1016/j.techfore.2015.01.012
Hidayat, K., Witanti, W., & Ramadhan, E. (2025). Analisis Tren dan Prediksi Penjualan Restoran Menggunakan Model Time Series Prophet. https://doi.org/10.47002/metik.v9i2.1101
Hossain, M. L., Shams, S. M. N., & Ullah, S. (2025). Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models. Discover Sustainability, 6(1). https://doi.org/10.1007/s43621-025-01733-5
Huang, Q. (2022). Forecasting Stock Prices Using Multi-Macroeconomic Regressors Based on the Facebook Prophet Model. BCP Business & Management, 25, 231–242. https://doi.org/10.54691/bcpbm.v25i.1762
Huang, S. (2025). Prophet with Exogenous Variables for Procurement Demand Prediction under Market Volatility. Journal of Computer Technology and Applied Mathematics, 2(6), 15–20. https://doi.org/10.70393/6a6374616d.333237
Kuang, Y. (2024). Time series data forecasting: Alcohol sales prediction based on Prophet. Highlights in Science, Engineering and Technology, 85, 597–601. https://doi.org/10.54097/x446dk79
Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers’ Decision-Making Process on Social Commerce Platforms: Online Trust, Perceived Risk, and Purchase Intentions [Review of Consumers’ Decision-Making Process on Social Commerce Platforms: Online Trust, Perceived Risk, and Purchase Intentions]. Frontiers in Psychology, 11. Frontiers Media. https://doi.org/10.3389/fpsyg.2020.00890
Liashenko, O., & Yakymchuk, B. (2023). The Application of Time-Series Forecasting Models in Grocery Retail Industry. Theoretical and Applied Issues of Economics, 47, 118–129. https://doi.org/10.17721/tppe.2023.47.11
Liu, B., Li, M., Ji, Z., Li, H., & Luo, J. (2024). Intelligent Productivity Transformation. Journal of Organizational and End User Computing, 36(1), 1–27. https://doi.org/10.4018/joeuc.336284
Luo, X., Lim, W. M., Cheah, J., Lim, X., & Dwivedi, Y. K. (2023). Live Streaming Commerce: A Review and Research Agenda. Journal of Computer Information Systems, 65(3), 376–399. https://doi.org/10.1080/08874417.2023.2290574
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013
Mandasari, I. C. S., Wardana, I. M., Ekawati, N. W., & Setiawan, P. Y. (2024). The role of live-streaming commerce in moderating the influence of entrepreneurial marketing and e-commerce adoption on the business performance of fashion SMEs. International Journal of Data and Network Science, 8(4), 2097–2106. https://doi.org/10.5267/j.ijdns.2024.6.021
Mazurek, M., Gauda, K., Skrzypek-Ahmed, S., Artiukov, A., & Stalinski, P. (2024). Collection Life Cycle Analysis: Enhancing Market Strategies through Advanced Data Analytics. EUROPEAN RESEARCH STUDIES JOURNAL, 14–24. https://doi.org/10.35808/ersj/3383
Nugraha, A., Pratiwi, M., Ardenis, D. M., Amany, N., Ahmad, S. S., & Sukmawati, A. (2024). Affiliate Marketer a Decent Work for Generation Z in Indonesia? TIJAB (The International Journal of Applied Business), 8(1), 45–55. https://doi.org/10.20473/tijab.v8.i1.2024.51251
Nugroho, A., Patty, M. A., & Karto, A. (2024). Development of Econometric Models for Financial Performance Forecasting in Companies. International Journal of Management Science and Information Technology, 4(2), 381–389. https://doi.org/10.35870/ijmsit.v4i2.3146
Pranata, J. A., Riyanto, M. R. P., Hendrawan, S., & Gunadi, W. (2024). The Effect of Hedonic Motivation and IT Affordance on Impulsive Buying Decision in Social Commerce TikTok. International Journal of Electronic Commerce Studies, 15(2), 62–102. https://doi.org/10.7903/ijecs.2303
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., ... Ziel, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001
Salman, D., Direkoğlu, C., Kuşaf, M., & Fahrioğlu, M. (2024). Hybrid deep learning models for time series forecasting of solar power. Neural Computing and Applications, 36(16), 9095–9112. https://doi.org/10.1007/s00521-024-09558-5
Shirazi, F., Hajli, N., Sims, J., & Lemke, F. (2022). The role of social factors in purchase journey in the social commerce era. Technological Forecasting and Social Change, 183, 121861–121861. https://doi.org/10.1016/j.techfore.2022.121861
Soltaninejad, M., Aghazadeh, R., Shaghaghi, S., & Zarei, M. F. E.-. (2024). Using Machine Learning Techniques to Forecast Mehram Company’s Sales: A Case Study. Journal of Business and Management Studies, 6(2), 42–53. https://doi.org/10.32996/jbms.2024.6.2.4
Suhud, U., Allan, M., Hoo, W. C., & Juliana, J. (2025). Purchase intention of Muslim consumers on TikTok live stream: Assessing the role of trust, reliability, and TikTok marketing activitie. International Journal of Data and Network Science, 9(4), 981–992. https://doi.org/10.5267/j.ijdns.2024.10.006
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080
Wang, P., & Huang, Q. (2022). Digital influencers, social power and consumer engagement in social commerce. Internet Research, 33(1), 178–207. https://doi.org/10.1108/intr-08-2020-0467
Yasser, A. S., & Gayatri, G. (2023). The Role of Live Streaming in Building Consumer Trust, Engagement, and Purchase Intention in Indonesian Social Commerce Thrift Clothes Sellers. ASEAN Marketing Journal, 15(2). https://doi.org/10.21002/amj.v15i2.1224
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