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Related Experiment Videos

Decoupling time and space: An adaptive shared graph convolutional network for dynamic market price forecasting.

Yalin Wang¹, Guodong Li¹, Chenliang Liu¹

¹School of Automation, Central South University, Changsha, 410083, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 27, 2025

Summary

This study introduces a novel spatiotemporal decoupling adaptive-shared graph convolutional network (STDAsh-GCN) for accurate product price forecasting. The method enhances market trend prediction by better modeling complex spatial and temporal dynamics.

Keywords:

Adaptive feature aggregation Deep learning Graph convolutional network Product price forecasting Spatiotemporal decoupling strategy

Related Experiment Videos

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Product price forecasting is crucial for business strategy due to volatile supply and demand.
Traditional graph neural networks struggle with complex spatiotemporal dependencies in market dynamics.

Purpose of the Study:

To propose a novel spatiotemporal decoupling adaptive-shared graph convolutional network (STDAsh-GCN) for enhanced product price prediction.
To improve the modeling of continuous market evolution and spatial diffusion patterns.

Main Methods:

Developed STDAsh-GCN with a globally shared parameter mechanism for deep spatial-temporal representation decoupling.
Incorporated an adaptive feature aggregation module for dynamic node contribution assessment.
Integrated a shared attention mechanism to balance feature and adjacency influences.

Main Results:

The STDAsh-GCN model demonstrated superior performance in product price prediction.
Validated effectiveness on three real-world industrial datasets, including potassium sulfate production.
Outperformed existing state-of-the-art methods in extensive experiments.

Conclusions:

The proposed STDAsh-GCN effectively captures complex spatiotemporal dependencies for accurate price forecasting.
The adaptive and shared mechanisms enhance the model's ability to integrate salient features and structural information.
This method offers a significant advancement for enterprises in anticipating market trends and optimizing sales strategies.