Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers — Paper

Abstract

-cross Abstract: Utilities publish Estimated Times of Restoration (ETRs) for customer-facing storm outages, and their accuracy governs whether customers can make sound decisions about food, medical equipment, and relocation. Prior work treats ETR as static tabular regression in which each outage contributes one record, discarding the fact that every development of an outage, from crew assignment through dispatch, suspension, damage assessment and partial restoration, is recorded as a revision. We reformulate ETR prediction as longitudinal tabular regression and introduce a Longitudinal Tabular Transformer (LTT), an axial-attention model that consumes the revisions preceding a prediction and issues a refined estimate at every one. On 242{,}928 storm-attributed outages from a cohort of 526{,}468 filtered events and 10.0 million revisions at six operating companies, LTT reduces customer-weighted asymmetric error at all six, by a median of 36.9\,\% against the estimates the utilities published during the same storms and 11.3\,\% against the strongest learned baseline at each. It is the only method improving on the incumbent's satisfaction impact at all six companies while also reducing root mean squared error at all six. Stratification by revision index shows that LTT error is largest at the first revision, where no history is available, and falls monotonically as revisions accumulate.

Authors 3

Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls Benjamin

Specification

Official page: arxiv.org/abs/2505.00225
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
arXiv id: 2505.00225
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Categories: cs.LG, cs.AI
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
PDF: arxiv.org/pdf/2505.00225
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh

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T19

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4 h ago

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Authors: Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls Benjamin

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Value	Valid from → to	Status	Source	Confidence	Extractor
Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls Benjamin	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperPredicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
New paper: Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

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