The paper introduces STFO (Spatio-Temporal Field Operator), a method for continual spatio‑temporal forecasting that decouples forecasting representations from specific sensor layouts. By normalizing coordinate‑based aggregation onto a fixed latent grid and using a spectral descriptor to adapt to process drift, STFO can reuse learned spatial maps across varying sensor configurations. Experiments on PEMS‑Stream, CA‑Stream, and AIR‑Stream show that STFO‑Large improves average MAE by 8.4% over DOL on PEMS‑Stream and 4.7% on CA‑Stream.
By Lewei Xie, Haoyu Zhang, Jiajun Zhou, Yulong Chen, Guanxing Chen, Yu-An Huang, Hau-San Wong, Yifan Zhang, Zhi-An Huang
AsyncCouple-Flow introduces a new framework for multi‑modal spatio‑temporal forecasting that tackles three key challenges: differing sampling rates, missing modalities, and autoregressive error accumulation. It employs a Modality‑Aware Token Sparsification module to produce equal‑length sequences, an Asynchronous Cross‑Modal Coupling Graph to fuse data under arbitrary asynchrony and missingness, and a Flow‑Matching Forecasting Head that models multi‑step prediction as a conditional ODE. Experiments on weather and traffic datasets demonstrate that the method outperforms state‑of‑the‑art baselines and remains robust even when up to two modalities are missing.
By Zhixiang Wu, Yining Liu, Bo Zhao, Szu-Yu Chen, Huiran Duan, Chu Lin, Chuanguang Yang
arXiv:2604. 15838v2 Announce Type: replace Abstract: Distribution shift severely degrades the performance of deep forecasting models.
By Zhaobo Hu, Vincent Gauthier, Mehdi Naima
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
By Lilan Peng, Yandi Liu, Qingren Yao, Chongshou Li, Tianrui Li
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
By Yongchao Li, Yang Li, Zhuoxuan Li, Jun Chen, Chu Zhang, Jinde Cao, Leszek Rutkowski
arXiv:2602.14049v2 Announce Type: replace-cross
Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...
By Yue Wang, Areg Karapetyan, Djellel Difallah, Samer Madanat
The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets demonstrate that DF-LLM outperforms existing methods in predictive accuracy.
By Xue Qiu, Jianli Xiao
The paper introduces SPALT, a method that models spatio‑temporal locality for multi‑step forecasting of geo‑referenced time series. SPALT uses linear model trees to group series with similar trends, injecting spatial features locally, and employs a Reduced Error Pruning strategy that respects spatio‑temporal locality. Experiments on three real‑world renewable‑energy datasets show SPALT outperforms both tree‑based models and state‑of‑the‑art neural networks in forecasting energy production at multiple horizons.
By Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci
The paper introduces DF-LLM, a Dynamic Fusion Large Language Model designed for traffic flow prediction. It combines a spatiotemporal embedding module, a fusion module that uses graph convolution to capture spatial topology and dynamic dependencies, and an LLM backbone with differentiated parameter adaptation and context aggregation attention. Experiments on four datasets show that DF-LLM outperforms existing methods in predictive accuracy.
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
By Lilan Peng, Yandi Liu, Qingren Yao, Chongshou Li, Tianrui Li
arXiv:2609.36119v1 Announce Type: new
Abstract: Spatial-temporal (ST) forecasting underpins many real-world systems such as traffic, climate, and energy networks. While existing methods implicitly as...
By Zhenyu Lei, Chenghao Liu, Yushun Dong, Qi R. Wang, Jundong Li
arXiv:2608. 05981v1 Announce Type: new Abstract: High-resolution climate data is crucial for meteorological predictions and for informing decision support across diverse domains.
By Yichen Zhang, Yixiong Xiao, Congxi Xiao, Jingbo Zhou