The paper introduces the Kronecker coVariance Neural Network (KVNN), a temporal graph neural network that models spatiotemporal covariance matrices as sums of Kronecker products, decoupling spatial and temporal dependencies. KVNNs perform filtering on spatial and temporal components, enabling expressive processing, rigorous spectral analysis, and provable stability to finite-sample estimation errors. Experiments on five real-world datasets show that KVNNs deliver strong forecasting performance with fewer trainable parameters than competing methods and maintain consistency under estimation noise.
arXiv:2608.20980v1 Announce Type: new Abstract: Graph neural networks (GNNs) are routinely employed for short-range forecasting on multivariate time series with a spatial graph structure. Despite the...
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction. Recent advances in Graph Neural Networks (GNNs) have demonstrated strong perfor- mance by assuming a static graph topology and aggregating information from neighboring series.
arXiv:2608. 07333v1 Announce Type: new Abstract: Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction.
arXiv:2607. 13108v1 Announce Type: cross Abstract: Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting.
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv:2609.25179v1 Announce Type: new Abstract: Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which ca...
arXiv:2603. 23746v2 Announce Type: replace Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges.
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spatial relationships b...
arXiv:2606. 13901v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics.
The paper introduces DNBNet, a Debiased Neural Basis-Function Network designed for irregular time series forecasting. It addresses two main limitations of existing methods: asymptotic bias from ignoring timestamp sampling density and limited adaptability of predefined basis functions. DNBNet employs importance sampling to correct bias, neural‑network parameterized basis functions for flexibility, a multi‑scale decomposition with mass‑aware fusion for sparse data, and a dual‑branch decoder, achieving strong performance across diverse real‑world datasets.
arXiv:2607. 19404v1 Announce Type: cross Abstract: Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learned representations as transient byproducts of prediction, leaving the organizational geometry of these patterns underexploited.