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arXiv Machine Learning

Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series

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.

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More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting

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
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arXiv Machine Learning
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AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting

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
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arXiv Machine Learning
Sep 22

Gaussian Process Decorrelation for Spatiotemporal Deep Learning-Based Snow Water Equivalent Prediction

The paper proposes a method for predicting future snow water equivalent (SWE) across the Western United States by first removing spatial correlations using a Gaussian Process-based linear transformation, then training a long short-term memory (LSTM) neural network on the decorrelated data. This separation of spatial and temporal components improves predictive accuracy compared to baseline models. Additionally, the authors incorporate conformal prediction to provide distribution‑free uncertainty estimates for SWE forecasts.

By Colin Fenster, Adrienne Marshall, Soutir Bandyopadhyay, Daniel McKenzie
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