<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Informer on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/informer/</link><description>Recent content in Informer on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 15 Dec 2024 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/informer/index.xml" rel="self" type="application/rss+xml"/><item><title>Time Series Forecasting (8): Informer — Efficient Long-Sequence Forecasting</title><link>https://www.chenk.top/en/time-series/informer-long-sequence/</link><pubDate>Sun, 15 Dec 2024 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/time-series/informer-long-sequence/</guid><description>&lt;p>&lt;figure class="article-figure">
 &lt;img src="https://blog-pic-ck.oss-cn-beijing.aliyuncs.com/posts/en/time-series/informer-long-sequence/illustration_1.png" alt="Time Series Forecasting (8): Informer — Efficient Long-Sequence Forecasting — Chapter overview" loading="lazy" decoding="async" class="content-image">
 
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&lt;p>The Transformer is wonderful at sequence modeling — right up to the moment your sequence gets long. Vanilla self-attention costs &lt;span class="math-inline">$\mathcal{O}(L^2)$&lt;/span>
 in both compute and memory, so a one-week hourly window (168 steps) is fine, a one-month window (720 steps) is painful, and a three-month window (2160 steps) is essentially impossible on a single GPU. That is exactly the regime real-world long-horizon forecasting lives in: weather, energy, finance, IoT.&lt;/p></description></item></channel></rss>