Prediction-powered inference for time series across space
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have access to unlabeled covariates ov...
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
Chronos-2 is a foundation model for multivariate time‑series forecasting that captures interactions among multiple series and heterogeneous covariates, enabling zero‑shot probabilistic forecasts without task‑specific tuning. It achieves state‑of‑the‑art accuracy, improving skill scores by 5.2 percentage points and win rates by 10 percentage points over leading baselines, while being computationally efficient—generating forecasts for 100 series in under one second on a single mid‑range GPU. The model demonstrates strong performance across diverse scientific domains such as infectious‑disease forecasting, macroeconomics, carbon‑flux prediction, and runoff simulation, matching or surpassing specialized methods.
arXiv:2606. 13119v1 Announce Type: cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
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...