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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Chronos: Learning the Language of Time Series · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2403.07815/</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/2403.07815/feed.xml" rel="self" type="application/rss+xml" /><item><title>Chronos: Learning the Language of Time Series — mentioned by rockwotj</title><link>https://news.ycombinator.com/item?id=47583289</link><guid isPermaLink="false">urn:hackerpapers:mention:2403.07815:47583289</guid><pubDate>Tue, 31 Mar 2026 06:00:35 GMT</pubDate><description>&lt;p&gt;https://www.datadoghq.com/blog/datadog-time-series-foundatio... https://moment-timeseries-foundation-model.github.io/ https://arxiv.org/abs/2403.07815 A friend at work used one to predict when our CEO would post in Slack, which is verry entertaining to see if correct.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2403.07815/"&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>Chronos: Learning the Language of Time Series — mentioned by rcmcintosh</title><link>https://news.ycombinator.com/item?id=41246808</link><guid isPermaLink="false">urn:hackerpapers:mention:2403.07815:41246808</guid><pubDate>Wed, 14 Aug 2024 14:53:08 GMT</pubDate><description>&lt;p&gt;Chronos: Learning the Language of Time Series: &amp;quot;We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2403.07815/"&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>Chronos: Learning the Language of Time Series — HN submission</title><link>https://news.ycombinator.com/item?id=39787176</link><guid isPermaLink="false">urn:hackerpapers:submission:2403.07815:39787176</guid><pubDate>Fri, 22 Mar 2024 03:25:13 GMT</pubDate><description>&lt;p&gt;Chronos: Learning the Language of Time Series&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;207 points; 59 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2403.07815/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chronos: Learning the Language of Time Series — HN submission</title><link>https://news.ycombinator.com/item?id=39740831</link><guid isPermaLink="false">urn:hackerpapers:submission:2403.07815:39740831</guid><pubDate>Mon, 18 Mar 2024 06:00:02 GMT</pubDate><description>&lt;p&gt;Chronos: New ML Framework for Pretrained Probabilistic Time Series Models&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;9 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2403.07815/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chronos: Learning the Language of Time Series — HN submission</title><link>https://news.ycombinator.com/item?id=39701376</link><guid isPermaLink="false">urn:hackerpapers:submission:2403.07815:39701376</guid><pubDate>Thu, 14 Mar 2024 07:37:23 GMT</pubDate><description>&lt;p&gt;Chronos: Learning the Language of Time Series&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;6 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2403.07815/"&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>
