Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 22 references
Abstract
Spatio-temporal time series forecasting is an important task in many areas such as traffic networks, environmental monitoring, and social networks. The goal is to predict future values of signals on the nodes of a graph using past observations. Current temporal encoding methods assume that temporal patterns are the same for all nodes. In practice, however, different nodes can show different temporal behavior depending on where they are in the graph and what their neighbors look like. In this work, we propose Graph-Enhanced Embeddings, a method that produces temporal embeddings based on the node's own signal and its neighbors' signals. The method uses learnable sinusoidal bases and a linear trend term together with a gating mechanism that controls which temporal bases are active for each node. We test the method on three benchmark datasets and show that it consistently outperforms standard baselines including fixed positional encodings, linear features, and Time2Vec.
The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.
J. Sun, Y.-J. Liu, Y.-L. Dou et al.· Advanced Electromagnetics· 0 citations
This work proposes \sysname, an Orbit-Adaptive Graph Neural Network framework, which outperforms existing transfer and naive zero-shot baselines, establishing a new paradigm for structure-driven zero-shot forecasting.
Yue Xu, Wen-Ying Duan, Xiaoxi He et al.· Proceedings of the 32nd ACM...· 0 citations
This paper proposes FAST-GNN, a graph neural network that integrates frequency-aware and spatio-temporal adaptive mechanisms to address the challenges of modeling complex spatio-temporal dependencies in multivariate time series forecasting. Traditional GNNs often struggle with dynamic graph construction, frequency-domain feature extraction, and noise robustness. Our approach innovatively introduces a multi-band driven dynamic graph construction mechanism to finely capture multi-scale correlations, employs a dual-granularity adaptive graph filter to distinguish and integrate local and global dependencies, and adopts a hierarchical graph reading strategy to enhance robustness against noise and anomalies. Extensive experiments on real-world datasets, including Climate, Electricity, Weather, and PEMS series, demonstrate that FAST-GNN significantly outperforms state-of-the-art baselines in both long- and short-term forecasting tasks, particularly showing stable and superior performance in challenging scenarios such as high-noise environments and long-term horizons.
Shuming Zhang· International Conference on...· 0 citations
Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlying nonlinear function approximator has received comparatively less attention. In this work, we propose STKAN, a spatio-temporal forecasting architecture that introduces Taylor-polynomial Kolmogorov--Arnold Network modules into spatial and temporal token mixing. STKAN first constructs high-level spatial representations through a learnable soft node-group assignment mechanism, applies group-wise spatial mixing, and subsequently models temporal dependencies over the compressed sequence. Spatial and temporal self-attention layers are further employed to capture long-range interactions. Experiments on five traffic forecasting benchmarks show that STKAN achieves competitive performance and performs better than the evaluated MLP-based variant in the tested settings. These results suggest that the design of nonlinear function approximators can serve as a useful complement to architectural design in spatio-temporal forecasting.
Probabilistic spatio-temporal forecasting is a pivotal challenge in the data mining community. Recently, diffusion models have emerged as a powerful paradigm in this field, acting as conditional generative models. Beyond standard diffusion models, a significant research trend involves tailoring the diffusion model to incorporate domain-specific inductive biases. However, for complex spatio-temporal data, the high-dimensional coupling between spatial and temporal domains makes the design of such models exceptionally difficult. In the present work, we propose a novel framework (GODM) that adopts orthogonalization to decouple intricate temporal dynamics while employing spatial convolutions to simulate information flow across space. By embedding the spatio-temporal structure directly into the forward process, the GODM introduces crucial inductive biases with negligible computational overhead. Extensive experiments on multiple benchmark datasets demonstrate that the GODM framework consistently outperforms state-of-the-art spatio-temporal diffusion models.
Zhixian Wang, Michel Ferreira Cardia Haddad, Jose Eduardo Medina Reyes et al.· Proceedings of the 32nd ACM...· 0 citations
To effectively integrate spatial and temporal representations, the proposed Spatio-Temporal Unified Network (STUNet) introduces query-aggregate attention, which simulates the process of tracing upstream and downstream nodes and aggregating their information, thereby capturing complex spatio-temporal dependencies.
Yujun Chen, Shihao Tu, Wen-Yu Ding et al.· Proceedings of the 32nd ACM...· 0 citations
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