HiPPO-KAN-GNN: a novel graph neural networks-based extension to Temporal Kolmogorov-Arnold Networks for multivariate time series forecasting
Author(s)
Joshi, Devvrat
Shukla, Pancham
Type
Conference Paper
Abstract
Time series forecasting faces challenges from non-stationarity, complex dependencies, and computational demands. Temporal Kolmogorov-Arnold Networks (TKANs) have emerged as a promising alternative to traditional deep learning models, leveraging learnable univariate functions for improved parameter efficiency and interpretability. However, a systematic comparison of TKAN variants is lacking. This paper presents the first comprehensive evaluation of state-of-the-art TKAN models, including TimeKAN, RMoK, and MT-KAN, across large-scale benchmark datasets. We identify a shared limitation in modeling inter-variable dependencies for multivariate forecasting and introduce HiPPO-KAN-GNN, a novel extension incorporating Graph Neural Networks to address this issue. Experiments on a high-performance computing cluster demonstrate consistent accuracy improvements with our model. Additionally, we conduct a sustainability analysis, measuring energy consumption and carbon footprint, offering critical insights for green AI deployment. This study provides a holistic view of TKANs’ performance, scalability, and environmental impact, advancing both their practical application and sustainable machine learning research.
Date Issued
2026-01-02
Date Acceptance
2025-08-08
Citation
Intelligent Sustainable Systems, 2026, 1727, pp.208-216
ISSN
2367-3370
Publisher
Springer
Start Page
208
End Page
216
Journal / Book Title
Intelligent Sustainable Systems
Volume
1727
Copyright Statement
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
World Conferences on Information Communication Systems, Software, Security and Sustainability (WorldS4)
Publication Status
Published
Start Date
2025-08-19
Finish Date
2025-08-21
Coverage Spatial
London, UK
Date Publish Online
2026-01-02