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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2506.02389/</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/2506.02389/feed.xml" rel="self" type="application/rss+xml" /><item><title>Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting — mentioned by thesz</title><link>https://news.ycombinator.com/item?id=46661204</link><guid isPermaLink="false">urn:hackerpapers:mention:2506.02389:46661204</guid><pubDate>Sat, 17 Jan 2026 19:28:19 GMT</pubDate><description>&lt;p&gt;[1] https://towardsdatascience.com/llm-powered-time-series-analy... [2] https://arxiv.org/abs/2506.02389 [3] https://arxiv.org/html/2402.10835v3 Some links from the top of Google search. Take a look here, also, it&amp;#x27;s an important law: https://en.wikipedia.org/wiki/Benford%27s_law It is possible for LLMs to learn Bernford&amp;#x27;s law, implicitly. So they will be non-null predictors of time series data, because time series data is also Bernford-law-distributed [4]. [4] https://ui.adsabs.harvard.edu/abs/2017EGUGA..19.2950T/abstra...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2506.02389/"&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>
