ItemConference Paper

Compressed Spatio Temporal Graph Neural Networks for Multivariate Time-Series Forecasting

Title
Compressed Spatio Temporal Graph Neural Networks for Multivariate Time-Series Forecasting
Creator
Binu, Katherin; Sahni, Prem; Vidushi; Choudhary, Arun Kumar
Description
Precise traffic flow forecasting is crucial for efficient transportation management and traffic congestion alleviation. Existing models typically fail to process the intricate spatial- temporal relationships in traffic data and thus incur compromised prediction performance. In this work, we introduce a Compressed Spatial-Temporal Enhanced Graph Neural Network (Comp-STEMGNN) to overcome these limitations. Our model combines 1D convolution-based temporal compression with graph neural networks to compress redundant time-series information without compromising vital patterns. Graph convolutional layers and temporal convolutional blocks extract the spatial and temporal relationships and facilitate efficient learning from enormous sensor networks. Experimental comparisons on benchmark traffic datasets show that Comp-STEMGNN outperforms existing approaches in forecasting accuracy while enjoying substantial computational complexity reduction. These findings identify its potential in real-time traffic forecasting and intelligent transportation systems. 2025 IEEE.
Source
Proceedings of 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical, Electronics and Computer Engineering, UPWIECON 2025;pp.601-606
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Comp-STEMGNN; Graph convolution; Road traffic forecasting; Spatio-temporal graph neural networks
Coverage
Binu K., School of Sciences, Christ University, Karnataka, Bengaluru, India; Sahni P., School of Sciences, Christ University, Karnataka, Bengaluru, India; Vidushi, School of Sciences, Christ University, Karnataka, Bengaluru, India; Choudhary A.K., Ministry of New and Renewable Energy, Government of India, New Delhi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156628-9;
Format
online
Language
English
Type
Conference paper

Citation

Binu, Katherin; Sahni, Prem; Vidushi; Choudhary, Arun Kumar, “Compressed Spatio Temporal Graph Neural Networks for Multivariate Time-Series Forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26220.

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