This article examines the methodological aspects of applying Transformer architectures for sales forecasting, demand analysis, and price optimization. The focus is on the development, adaptation, and integration of models in the context of processing large volumes of data and operating complex market mechanisms. The paper explores approaches to combining time series, identifying factor relationships, and improving the accuracy of analytical conclusions.

The methodology includes adapting basic Transformer architectures, such as Transformer with Multihead Attention Mechanism, to the specific characteristics of the data. The preparatory steps cover information aggregation, creation of temporal features, identification of categorical variables, and handling missing data. Historical datasets supplemented with external information sources are used for training. The sources include scientific articles by international authors published in open access, as well as materials available on the internet, allowing for a broad examination of the topic.

The results demonstrate the effectiveness of these architectures in forecasting tasks, identifying temporal dependencies, and improving business process quality. Examples of model implementation illustrate their successful use in commercial information systems. The conclusions emphasize the role of these approaches in decision-making automation and strategic planning.

The materials of the article are intended for professionals working in machine learning, data analytics, and process management improvement.

sales forecasting, demand modeling, price optimization, time series, machine learning, deep learning, Transformer architecture

Amellal I. et al. An integrated approach for modern supply chain management: utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction //Decision Science Letters. – 2024. – Vol. 13(1). – pp. 237-248.

Li Q., Yu M. Achieving sales forecasting with higher accuracy and efficiency: A new model based on modified transformer //Journal of Theoretical and Applied Electronic Commerce Research. – 2023. – Vol. 18 (4). – pp. 1990-2006.

Mu S. et al. Transformative computing for products sales forecast based on SCIM //Applied Soft Computing. – 2021. – Vol. 109. – pp. 1- 13.

Cui E. et al. Fertilizer sales forecasting model based on transformer-BiGRU //Third International Conference on Machine Learning and Computer Application (ICMLCA 2022). – SPIE. - 2023. – Vol. 12636. – pp. 441-446.

Celeita Rodriguez D. F. An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture. – 2023. - №. 16. - pp. 1-24.

Zhang Z., Wu M. Real-time locational marginal price forecast: A decision transformer-based approach //2023 IEEE Power & Energy Society General Meeting (PESGM). – IEEE. - 2023. – pp. 1-5.

Xiang Y. et al. TSFRN: Integrated Time and Spatial-Frequency domain based on Triple-links Residual Network for Sales Forecasting //2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). – IEEE. - 2023. – pp. 1012-1019.

Zhong B. Deep learning integration optimization of electric energy load forecasting and market price based on the ANN–LSTM–transformer method //Frontiers in Energy Research. – 2023. – Vol. 11. – pp. 1-14.

Zhou H. et al. Informer: Beyond efficient transformer for long sequence time-series forecasting //Proceedings of the AAAI conference on artificial intelligence. – 2021. – Vol. 35 (12). – pp. 11106-11115.

Zhang J., Zhao J. Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making //Energies. – 2022. – Vol. 15 (12). – pp. 4222.

Taparia V. et al. Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model //Journal of Graphic Era University. – 2024. – pp. 15-36.

Smirnov P. S., Sudakov V. A. Forecasting new product demand using machine learning //Journal of Physics: Conference Series. – IOP Publishing, 2021. – Vol. 1925 (1). – pp. 1-8.