← Back to all news
arXiv AI

Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting

The paper introduces TEGER, a residual covariance model that updates a forecaster’s joint predictive uncertainty at test time using closed‑form Gaussian conditioning on recent residuals and an exponential moving‑average volatility term. It leverages a fixed sensor graph to encode spatial correlations without retraining the underlying forecasting backbone. Experiments on four traffic datasets show significant improvements in 60‑minute CRPS, and applying TEGER to a frozen foundation model (Chronos) reduces CRPS from 0.1798 to 0.1736.

Related stories

arXiv AI
Aug 24

RiskTraf: Risk-Extrapolated Residual Learning for Multi-Variate Traffic Flow Prediction

RiskTraf introduces a risk-extrapolated residual learning approach for multi-variate traffic flow prediction, leveraging raw flow, speed, and occupancy data from the new PEMSB-3V benchmark. The method freezes a trained spatio-temporal backbone and adds a lightweight residual head that learns from historical speed and occupancy to correct flow predictions across different traffic regimes. Experiments show consistent improvements over various backbones and outperform existing debiasing and distribution-shift adaptation techniques.

By Guangyu Wang, Zhidan Liu
benchmarks
More like this →
arXiv AI
Oct 27

The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting

The paper introduces Multivariate Time Series Super-Resolution Forecasting (MTS‑SRF), a method that predicts fine‑grained future values from coarse historical data. It proposes the TimeMagnifier framework, which includes Fourier Anchor to handle data sparsity, De‑noise Only to refine predictions along the resolution axis, and banded DTW Alignment to correct cross‑resolution misalignment. On a new benchmark of ten datasets across five domains, TimeMagnifier outperforms 15 baselines, reducing mean‑squared error by an average of 12.3% and improving existing forecasters by 5.1% to 9.0% when its components are used as plug‑ins.

By Shuhao Li, Weidong Yang, Yue Cui, Fan Zhang, Lu Qin
efficiencybenchmarkssafety
More like this →
arXiv AI
Aug 24

SPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers

SPARC (Single-Pass Adaptive Risk Calibration) is a Bayesian‑conformal uncertainty layer for human motion forecasting that adds an analytic epistemic scale to a deterministic MLP backbone’s Gaussian covariance. The scale, κ_t(x), inflates the covariance without altering its correlation structure, enabling 95% marginal prediction tubes with finite‑sample validity via split conformal calibration. Across nine dataset‑protocol blocks, SPARC outperforms baselines on NLL and a combined MPJPE+NLL metric while maintaining competitive point accuracy and efficient calibrated tubes.

By Sakif Hossain, Julian Teusch, J\"org P. M\"uller
multimodal
More like this →
arXiv Machine Learning
Sep 16

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
diffusionnlpmultimodalbenchmarks
More like this →