Leveraging Large Language Models for Indonesian Retail Sales Probabilistic Forecasting

Zhang, W. Implementation of Bigdata Techniques in Sales Forecasting: Evidence from Retailing and E-commerce. Advances in Economics, Management and Political Sciences 2024, 128. https://doi.org/10.54254/2754-1169/2024.18262.

Sharma, H.; et al. Enhancement of Sales Forecasting and Prediction with Machine Learning Methods. International research journal of computer science 2024, 11. https://doi.org/10.26562/irjcs.2024.v1111.02.

Gao, Y. A Comparative Study of ARIMA and ETS Models for Time Series Forecasting. Advances in Economics, Management and Political Sciences 2025, 149. https://doi.org/10.54254/2754-1169/2024.19252.

Teoh, T.T.; Cho, S.Y.; Nguwi, Y.Y. Emotional prediction using time series multiple-regression genetic algorithm for autistic syndrome disorder. In Proceedings of the 2012 7th International Conference on Computer Science Education (ICCSE), 2012, pp. 9–12. https://doi.org/10.1109/ICCSE.2012.6295015.

Khashei, M.; Bijari, M.; Ardali, G.A.R. Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs). Neurocomputing 2009, 72, 956–967.

Ye, J.; Yang, L. A comparative study of ensemble support vector regression methods for short-term load forecasting. In Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018, pp. 139–143.

Tang, T.; Liu, T.; Gui, G. Forecasting precipitation and temperature evolution patterns under Climate Change using a Random Forest Approach with Seasonal Bias correction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024.

Hossain, M.S.; et al. Enhanced market trend forecasting using machine learning models: a study with external factor integration. Journal of global education and research 2025, 06. https://doi.org/10.55640/business/volume06issue01-02.

Ni, L.; Li, Y.; Wang, X.; Zhang, J.; Yu, J.; Qi, C. Forecasting of forex time series data based on deep learning. Procedia computer science 2019, 147, 647–652.

Nasution, A.H.; Onan, A. ChatGPT Label: Comparing the Quality of Human-Generated and LLM-Generated Annotations in Low-Resource Language NLP Tasks. IEEE Access 2024, 12, 71876–71900. https://doi.org/10.1109/ACCESS.2024.3402809.

Khalila, Z.; Nasution, A.H.; Monika, W.; Onan, A.; Murakami, Y.; Radi, Y.B.I.; Osmani, N.M. Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models. International Journal of Advanced Computer Science and Applications 2025, 16. https://doi.org/10.14569/IJACSA.2025.01602134.

Nasution, A.H.; Monika, W.; Onan, A.; Murakami, Y. Benchmarking 21 Open-Source Large Language Models for Phishing Link Detection with Prompt Engineering. Information 2025, 16. https://doi.org/10.3390/info16050366.

Brown, T.B.; et al. Language models are few-shot learners. In Proceedings of the Advances in Neural Information Processing Systems, 2020, Vol. 33, pp. 1877–1901.

Hidayat, F.; Nasution, A.H.; Ambia, F.; Putra, D.F.; Mulyandri. Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches. IEEE Access 2025, 13, 65643–65658. https://doi.org/10.1109/ACCESS.2025.3560254.

Zhou, Q.; et al. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. In Proceedings of the Proceedings of the 38th International Conference on Machine Learning, 2021, Vol. 139.

Khairurrahman, A.L.; Badriah, L.S.; Sambodo, H.; Rahajuni, D.; Kadarwati, N.; Pudjianto, H.; Setiarso, O.; Supriadi, D.; Priyono, R. Informative Industrial Analytic for Effective Retail Business Performance: A Case of Emerging Economy. WSEAS Transactions on Systems 2023, 22, 170–179.

Cheng, K.M.; Wijaya, L.; Ng, K.C.; Anthonysamy, L. Decoding Consumer Behaviour in Indonesian E Commerce: A Stimulus-Organism-Response Analysis. Australian Journal of Telecommunications and the Digital Economy 2024, 12. https://doi.org/10.18080/jtde.v12n4.1009.

Suresh, B.; Suresh, M. A comprehensive analysis of retail sales forecasting using machine learning and deep learning methods. In Proceedings of the 2023 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2023, pp. 1–5.

Ganguly, P.; Mukherjee, I. Enhancing Retail Sales Forecasting with Optimized Machine Learning Models. In Proceedings of the 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024, pp. 884–889.

Bank Indonesia. Laporan Survei Penjualan Eceran (SPE) Januari 2025, 2025. [Online]. Available: https://www.bi.go.id/id/publikasi/laporan/Pages/SPE-Januari-2025.aspx. [Accessed Mar. 11, 2025].

Pranolo, A.; et al. Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques. Ilkom Jurnal Ilmiah 2024, 16. https://doi.org/10.33096/ilkom.v16i2.2333.210-220.

Jantima, P.; Bancha, L.; Khanista, N. Comparative Study for Data Normalization Methods on Predicting Cryptocurrency Price. -B: 2022, 13, 853.

Phan, Q.T.; Wu, Y.K.; Phan, Q.D. An Overview of Data Preprocessing for Short-Term Wind Power Forecasting. In Proceedings of the 2021 7th International Conference on Applied System Innovation, ICASI 2021, 2021. https://doi.org/10.1109/ICASI52993.2021.9568453.

Caillault, É.P.; Bigand, A.; et al. Comparative study on univariate forecasting methods for meteorological time series. In Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018, pp. 2380–2384.

Pereira, P.J.; Costa, N.; Barros, M.; Cortez, P.; Durães, D.; Silva, A.; Machado, J. A comparison of automated time series forecasting tools for smart cities. In Proceedings of the EPIA Conference on Artificial Intelligence. Springer, 2022, pp. 551–562.

Bahrpeyma, F.; Roantree, M.; McCarren, A. Multi-resolution forecast aggregation for time series in agri datasets 2017.

Amjad, F.; Korotko, T.; Rosin, A. Forecasting PV Energy Generation Using Transformer-Based Architectures: A Comparative Study of Lag-Llama, TFT, and DeepAR. In Proceedings of the 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). IEEE, 2024, pp. 1–6.

Oliveira, J.M.; Ramos, P. Evaluating the Effectiveness of Time Series Transformers for Demand Forecasting in Retail. Mathematics 2024, 12, 2728.

Lim, B.; Arık, S.Ö.; Loeff, N.; Pfister, T. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting 2021, 37, 1748–1764.

Ansari, A.F.; Stella, L.; Turkmen, C.; Zhang, X.; Mercado, P.; Shen, H.; Shchur, O.; Rangapuram, S.S.; Arango, S.P.; Kapoor, S.; et al. Chronos: Learning the language of time series. arXiv preprint arXiv:2403.07815 2024.

Vishwanath, A.; Basheeruddin, M.; Saveetha, D. Forecasting Sales Data Using Time Series Models and LSTM Model. In Proceedings of the 2024 7th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), jul 2024. https://doi.org/10.1109/icait61638.2024.10690408.

Tello, A.; Izquierdo, I.; Pacheco, G.; Vanegas, P. Prediction of imports of household appliances in ecuador using LSTM networks. In Proceedings of the Information and Communication Technologies of Ecuador (TIC. EC). Springer, 2020, pp. 194–207.

Sonata, I.; Heryadi, Y. Comparison of LSTM and Transformer for Time Series Data Forecasting. In Proceedings of the 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024, pp. 491–495.

Schwartz, Z.; Ma, J.; Webb, T. The MSapeMER: a symmetric, scale-free and intuitive forecasting error measure for hospitality revenue management. International Journal of Contemporary Hospitality Management 2023. https://doi.org/10.1108/ijchm-01-2023-0088.

Castillo Estrada, M.d.R.; Gómez Camarillo, M.E.; Sánchez Parraguirre, M.E.; Gómez Castillo, M.E.; Meneses Juárez, E.; Cruz Gómez, M.J. Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises. International Journal of Business Administration 2020, 11. https://doi.org/10.5430/IJBA.V11N4P39.

Mathai, A.V.; Agarwal, A.; Angampalli, V.; Narayanan, S.; Dhakshayani, E. Development of new methods for measuring forecast error. International Journal of Logistics Systems and Management 2016, 24, 213–225.

Kovalevsky, V.E.; Zhukova, N.A. Building a Model for Time Series Forecasting using AutoML Methods. In Proceedings of the 2024 XXVII International Conference on Soft Computing and Measurements (SCM). IEEE, 2024.

Cui, W.; Wan, C.; Song, Y. Ensemble deep learning-based non-crossing quantile regression for nonparametric probabilistic forecasting of wind power generation. IEEE Transactions on Power Systems 2022, 38, 3163–3178.

Lopez-Martin, M.; Sanchez-Esguevillas, A.; Hernandez-Callejo, L.; Arribas, J.I.; Carro, B. Additive ensemble neural network with constrained weighted quantile loss for probabilistic electric-load forecasting. Sensors 2021, 21, 2979.

Shalihin, N.; Firdaus, F.; Yulia, Y.; Wardi, U. Ramadan and strengthening of the social capital of indonesian muslim communities. HTS Teologiese Studies / Theological Studies 2020, 76. https://doi.org/10.4102/HTS.V76I3.6241.

Krawiec, M.; Górska, A. Analysis of Seasonal Patterns in the Performance of Fuel Markets in the Visegrad Group. Comparative Economic Research 2024, 27. https://doi.org/10.18778/1508-2008.27.12.

Marx, D. Success on Repeat. Package Printing 2024, 71.

Hahn, M.; Rofin, M. Why are Sensitive Functions Hard for Transformers? In Proceedings of the Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2024, Vol. 1. https://doi.org/10.18653/v1/2024.acl-long.800.

Gagnon, E.; López-Salido, D. Small Price Responses to Large Demand Shocks. Journal of the European Economic Association 2020, 18. https://doi.org/10.1093/jeea/jvz002.