The rapid evolution of big data and artificial intelligence technologies is driving the penetration of deep learning methods into various aspects of business forecasting. This study provides a systematic review of the application of deep learning in business forecasting, constructing an analytical framework across three dimensions: theoretical architecture, practice pathways, and practical obstacles. From the theoretical perspective, this paper summarizes mainstream architectures including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and Transformers, and examines their adaptation mechanisms for time series forecasting. From the practice perspective, it investigates the typical application patterns and actual effectiveness of deep learning in scenarios such as retail sales estimation, demand and supply chain planning, and financial time series modeling. From the obstacle perspective, it discusses issues related to data quality, model interpretability gaps, computational resource constraints, and cross-scenario generalization performance. Overall, deep learning demonstrates significant advantages in business forecasting, but achieving large-scale deployment requires striking a balance between methodological robustness and business interpretability.
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Share and Cite
Zhang, Y. (2026) Application of Deep Learning in Business Forecasting: Theoretical Framework, Practice Pathways, and Challenges. Scientific Research Bulletin, 3(3), 41-45.