Transformers in Data-Driven Decision-Making: Applications for Forecasting Sales, Analyzing Demand, and Optimizing Pricing Strategies

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.