<?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 on SailingDataLakes</title><link>https://sailingdatalakes.com/tags/time-series/</link><description>Recent content in Time Series on SailingDataLakes</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 03 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sailingdatalakes.com/tags/time-series/index.xml" rel="self" type="application/rss+xml"/><item><title>Auto Regression</title><link>https://sailingdatalakes.com/posts/auto-regression/</link><pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate><guid>https://sailingdatalakes.com/posts/auto-regression/</guid><description>Purpose Link to heading In this article we&amp;rsquo;re covering Auto Regression (AR) - one of the foundational models used for time series forecasting. We&amp;rsquo;ll build up an intuition for what makes it different from the regression models we&amp;rsquo;ve covered previously, walk through the underlying math, and then implement it from scratch, keeping the code as close to the math as possible. We&amp;rsquo;ll wrap up by fitting our implementation to a real (and famous) dataset: the Wolf sunspot numbers.</description></item></channel></rss>