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Sustainability of TSFM Deployment and Pretraining: From Carbon Footprint Analysis to Mitigation via Pruning

Paper recorded by Signals 4 on 2026-10-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.LG · 机器学习 · first seen 2026-10-26

Abstract

Time Series Foundation Models (TSFMs) offer strong zero-shot forecasting capabilities, but their high pretraining cost raises a practical sustainability question: does this upfront investment pay off over a full deployment lifecycle, compared to supervised standalone models that must be periodically retrained? We model the cumulative carbon cost of different approaches across a realistic industria

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#190 most recent of 749 cs.LG papers we have recorded · ↑ newer: Trusting the Tool: Asymmetric Deference of LLM Vulnerability Auditors · ↓ older: Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
Cite this page: Sustainability of TSFM Deployment and Pretraining: From Carbon Footprint Analysis to Mitigation via Pruning: the #190 most recent of 749 cs.LG papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/sustainability-of-tsfm-deployment-and-pretraining-from-carbon-footprint-analysis.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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