GENERALIZED ARCHITECTURE OF A BI SYSTEM BASED ON DEEP LEARNING
DOI:
https://doi.org/10.47390/ts-v3i8y2025No5Keywords:
Business Intelligence (BI); Deep Learning; Data Preparation; LSTM; Linear Regression; Support Vector Regression (SVR); Hybrid Model; Forecasting System; Agricultural Product Prices; BI-Pred 1.0; Intelligent Analysis; Data Visualization; Strategic Decision MakingAbstract
This paper analyzes the overall architecture and key components of the software package “BI-Pred 1.0.” The study examines the application of data preparation, pre-processing, and machine learning algorithms within a hybrid deep-learning approach that integrates LSTM, Linear Regression, and Support Vector Regression (SVR) models. Based on this approach, the feasibility of forecasting agricultural product prices—particularly potato prices—is substantiated. The research results demonstrate that the effective use of intelligent data-analysis methods in economic processes can significantly facilitate strategic decision-making and improve forecasting accuracy
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