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Prediction-powered inference for time series across space

Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.LG · 机器学习 · first seen 2026-10-07

Abstract

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 over a longer time period. Data is observed over many spatial locations. For instance, crop yield might be observed over a large geographical area for recent years, but weather data (which is informativ

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#367 most recent of 762 cs.LG papers we have recorded · ↑ newer: Co-Evolving Paths and Flows via Path-Flow Alignment · ↓ older: GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
Cite this page: Prediction-powered inference for time series across space: the #367 most recent of 762 cs.LG papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/prediction-powered-inference-for-time-series-across-space.html
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