Online Reviews in SKU-level Demand Forecasting Using Global Cross-Learning LSTM
DOI:
https://doi.org/10.59261/bustechno.v7i4.755Keywords:
Global Cross-Learning LSTM, Intermittent Demand, Online Reviews, Proxy Theory, Sparsity BarrierAbstract
Background: Daily SKU-level demand forecasting is challenging in zero-inflated e-commerce data because absolute-error optimization can favor zero forecasts and fail to capture demand activation.
Objective: This study evaluates a Global Cross-Learning LSTM that combines transaction history, structural sparsity markers, and online-review signals as behavioral proxies.
Methods: Computational forecasting experiments used daily SKU-level transaction and review data from the Amazon and Olist datasets. Four ablation configurations (M0–M3) were compared with SES, Croston, and SBA using MASE, RMSSE, and AMSE. The LSTM used a 30-day lookback window, 50 units, the Adam optimizer, a batch size of 128, 20 epochs, and Huber loss. Paired differences across 22 entities were assessed using the Wilcoxon signed-rank test.
Results: Across the full timeline, M0 obtained the lowest MASE (0.4243), reflecting the advantage of zero forecasts on inactive days. During activation windows, however, M3 reduced AMSE from 0.7769 to 0.6195 and produced a statistically significant improvement in RMSSE (p < 0.05). Global pooling also supported forecasting for cold-start and lumpy-demand items.
Conclusion: The findings support Proxy Theory by indicating that review volume and valence can provide leading information before sufficient transaction data accumulate. Theoretically, the study links behavioral proxies with global representation learning for intermittent demand. Practically, the model can inform replenishment and safety-stock decisions by reducing underforecasting during demand activation. The novelty lies in jointly evaluating online-review proxies and global cross-learning at the SKU level under lifecycle-specific conditions.
References
Anoop, T. S., & Rahman, Z. (2025). Online impulse buying: a systematic review of 25 years of research using meta regression. Journal of Consumer Behaviour, 24(1), 363–391.
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Panagiotelis, A. (2024). Forecast reconciliation: A review. International Journal of Forecasting, 40(2), 430–456. https://doi.org/10.1016/j.ijforecast.2023.10.010
Caserta, M., & D’Angelo, L. (2025). Intermittent demand forecasting for spare parts with little historical information. Journal of the Operational Research Society, 76(2), 294–309.
Center, S. R. (2017). How Online Reviews Influence Sales: Evidence of the Power of Online Reviews to Shape Customer Behavior. Northwestern University, Medill Spiegel Research Center. https://spiegel.medill.northwestern.edu/how-online-reviews-influence-sales/
Di Fonzo, T., & Girolimetto, D. (2023). Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives. International Journal of Forecasting, 39(1), 39–57. https://doi.org/10.1016/j.ijforecast.2021.08.004
Jain, A., & Singh, S. (2026). Long tail patterns in online reviews for search and experienced goods: Evidence from Amazon. Acta Psychologica, 269, 107408.
Kolassa, S. (2016). Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting, 32(3), 788–803. https://doi.org/10.1016/j.ijforecast.2015.12.004
Kourentzes, N., & Athanasopoulos, G. (2021). Elucidate structure in intermittent demand series. European Journal of Operational Research, 288(1), 141–152. https://doi.org/10.1016/j.ejor.2020.05.046
Li, K., Chen, Y., & Zhang, L. (2020). Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category. Journal of Retailing and Consumer Services, 55, 102107. https://doi.org/10.1016/j.jretconser.2020.102107
Lolli, F., Gamberini, R., Regattieri, A., Balugani, E., Gatos, T., & Gucci, S. (2017). Single-hidden layer neural networks for forecasting intermittent demand. International Journal of Production Economics, 183, 116–128. https://doi.org/10.1016/j.ijpe.2016.10.021
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
Nugroho, A., & Wang, W.-T. (2024). Credibility of YouTube product reviews and customers’ product return intention: A product fit uncertainty perspective. Information Technology & People, 37(4), 1560–1586. https://doi.org/10.1108/ITP-11-2021-0838
Pattnaik, M., Kumar Padhi, S., Panda, L., Naushad, U., Behera, R. K., Mishra, A., & Pattnaik, A. (2026). AI-driven sales forecasting in retail: insights from Big Bazaar’s sales data. International Journal of Management Science and Engineering Management, 21(1), 1–14.
Sahoo, N., Dellarocas, C., & Srinivasan, S. (2018). The impact of online product reviews on product returns. Information Systems Research, 29(3), 723–738. https://doi.org/10.1287/isre.2017.0736
Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75–85. https://doi.org/10.1016/j.ijforecast.2019.03.017
Spiliotis, E., Makridakis, S., Semenoglou, A.-A., & Assimakopoulos, V. (2022). Comparison of statistical and machine learning methods for daily SKU demand forecasting. Operational Research, 22(3), 3037–3061. https://doi.org/10.1007/s12351-020-00605-2
Wang, Y., Yu, B., & Chen, J. (2026). Effects of product online reviews on product returns: A review and classification of the literature. International Transactions in Operational Research, 33(1), 143–176. https://doi.org/10.1111/itor.13502
Yang, G., Li, K., Zhang, Y., & Feng, Q. (2024). End-to-End Modeling and Long Short-Term Memory Application in Time Series Modeling: Modeling and Prediction of Electronic Commerce User Behavior. Journal of Organizational and End User Computing, 36(1), 1–27. https://doi.org/10.4018/JOEUC.349732
Yang, Y. (2025). Data-driven prediction of future purchase behavior in cross-border e-commerce using sequence modeling with PSO-tuned LSTM. Plos One, 20(12), e0337932.
Zhang, G. P., Xia, Y., & Xie, M. (2024). Intermittent demand forecasting with transformer neural networks. Annals of Operations Research, 339(1), 1051–1072.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ivan Michael Siregar, Irfan Muhammad Fauzi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the 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 acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
