<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Time-Intelligence on TJ Brown | Tech Blog</title><link>https://tjbrown.ai/tags/time-intelligence/</link><description>Recent content in Time-Intelligence on TJ Brown | Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 06 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://tjbrown.ai/tags/time-intelligence/index.xml" rel="self" type="application/rss+xml"/><item><title>Period-to-Date Calculations in Metric Views: A DAX Developer's Guide</title><link>https://tjbrown.ai/posts/05-metric-views-period-to-date/</link><pubDate>Fri, 06 Mar 2026 00:00:00 +0000</pubDate><guid>https://tjbrown.ai/posts/05-metric-views-period-to-date/</guid><description>&lt;p&gt;In the last post we covered period-over-period growth. This post is about time intelligence: running totals, year-to-date, and trailing windows.&lt;/p&gt;
&lt;p&gt;If you&amp;rsquo;ve written DAX time intelligence functions, you already understand the &lt;em&gt;intent&lt;/em&gt; of these calculations. The challenge in DAX is usually the mechanics — filter context, &lt;code&gt;CALCULATE&lt;/code&gt;, &lt;code&gt;ALL&lt;/code&gt;, &lt;code&gt;DATESYTD&lt;/code&gt;. In Metric Views, you declare what you want and the engine handles the rest.&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id="the-window-block"&gt;The &lt;code&gt;window&lt;/code&gt; Block&lt;/h2&gt;
&lt;p&gt;Window measures extend a standard measure with a &lt;code&gt;window:&lt;/code&gt; block. The block controls how the measure aggregates across an ordered dimension — typically a date.&lt;/p&gt;</description></item></channel></rss>