Article: Dynamic path transformer network for regional economic forecasting and resource allocation Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.23 pp.24 - 39 Abstract: In regional economic planning, accurate forecasting and efficient resource allocation are vital for informed decision-making by both government and private sectors. The article titled 'Dynamic path transformer network for regional economic forecasting and resource allocation' presents a novel deep learning-based approach that leverages transformer architecture to enhance forecasting precision and optimise the allocation of resources. Central to this study is the dynamic path transformer network (DPTN), which effectively captures complex spatial-temporal economic data through attention mechanisms that dynamically weigh economic indicators. This design allows the model to adapt to changing economic conditions and deliver more accurate predictions than traditional statistical or machine learning models. The study benchmarks DPTN against conventional approaches and demonstrates its superior performance in predictive accuracy and resource management. Moreover, the paper explores the broader implications for policy formulation and strategic planning, while also addressing key challenges such as data limitations, computational demands, and interpretability. Inderscience Publishers - linking academia, business and industry through research

Title: Dynamic path transformer network for regional economic forecasting and resource allocation

Authors: Ronghai Sun

Addresses: Business School, Zhuhai College of Science and Technology, ZhuHai, 51904, GuangDong, China

Abstract: In regional economic planning, accurate forecasting and efficient resource allocation are vital for informed decision-making by both government and private sectors. The article titled 'Dynamic path transformer network for regional economic forecasting and resource allocation' presents a novel deep learning-based approach that leverages transformer architecture to enhance forecasting precision and optimise the allocation of resources. Central to this study is the dynamic path transformer network (DPTN), which effectively captures complex spatial-temporal economic data through attention mechanisms that dynamically weigh economic indicators. This design allows the model to adapt to changing economic conditions and deliver more accurate predictions than traditional statistical or machine learning models. The study benchmarks DPTN against conventional approaches and demonstrates its superior performance in predictive accuracy and resource management. Moreover, the paper explores the broader implications for policy formulation and strategic planning, while also addressing key challenges such as data limitations, computational demands, and interpretability.

Keywords: economic forecasting; deep learning; transformer network; resource allocation; spatial-temporal analysis.

DOI: 10.1504/IJICT.2025.147125

International Journal of Information and Communication Technology, 2025 Vol.26 No.23, pp.24 - 39

Received: 17 Mar 2025
Accepted: 16 Apr 2025
Published online: 10 Jul 2025 *

Title: Dynamic path transformer network for regional economic forecasting and resource allocation

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