<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Time Series | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/time-series/</link><atom:link href="https://ahren09.github.io/tags/time-series/index.xml" rel="self" type="application/rss+xml"/><description>Time Series</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 14 Oct 2026 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Time Series</title><link>https://ahren09.github.io/tags/time-series/</link></image><item><title>TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models</title><link>https://ahren09.github.io/publication/preprint26_timersi/</link><pubDate>Wed, 14 Oct 2026 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/preprint26_timersi/</guid><description>&lt;h2 id="overview">Overview&lt;/h2>
&lt;p>TimeRSI develops task-specific forecasting pipelines around frozen time-series foundation models. A coding agent translates task descriptions into executable changes, evaluates revisions on historical forecasting windows, and uses forecast errors to guide further improvements. At deployment, an adaptive selector chooses among the resulting pipelines using feedback from completed forecasts under similar conditions.&lt;/p>
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