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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Unified Training of Universal Time Series Forecasting Transformers · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2402.02592/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2402.02592/feed.xml" rel="self" type="application/rss+xml" /><item><title>Unified Training of Universal Time Series Forecasting Transformers — mentioned by pbronez</title><link>https://news.ycombinator.com/item?id=39827822</link><guid isPermaLink="false">urn:hackerpapers:mention:2402.02592:39827822</guid><pubDate>Tue, 26 Mar 2024 13:51:35 GMT</pubDate><description>&lt;p&gt;References this paper on Time Series transformers. First I’ve seen someone apply transformers to time series specifically. Very curious how well this might work for low-frequency events. https://arxiv.org/abs/2402.02592&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2402.02592/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
