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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2505.23719/</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/2505.23719/feed.xml" rel="self" type="application/rss+xml" /><item><title>TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning — mentioned by nextos</title><link>https://news.ycombinator.com/item?id=44504299</link><guid isPermaLink="false">urn:hackerpapers:mention:2505.23719:44504299</guid><pubDate>Tue, 08 Jul 2025 21:41:18 GMT</pubDate><description>&lt;p&gt;Second-generation LSTMs (xLSTM) do have leading performance on zero-shot time series forecasting: https://arxiv.org/abs/2505.23719. I think other architectures, aside from the transformer, might lead to SOTA performance, but they remain a bit unexplored.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2505.23719/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning — HN submission</title><link>https://news.ycombinator.com/item?id=44158302</link><guid isPermaLink="false">urn:hackerpapers:submission:2505.23719:44158302</guid><pubDate>Mon, 02 Jun 2025 12:52:35 GMT</pubDate><description>&lt;p&gt;TiRex: Zero-Shot Forecasting Across Long and Short Horizons&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2505.23719/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
