https://ai-blog.noorshomelab.dev/tags/time-series/Recent content in Time-Series on AI VOIDHugoenMon, 26 Jan 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-compressing-time-series-iot/Mon, 26 Jan 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-compressing-time-series-iot/<h2 id="introduction-shrinking-the-iot-data-deluge">Introduction: Shrinking the IoT Data Deluge</h2> <p>Welcome back, intrepid data explorer! In this chapter, we&rsquo;re diving into a crucial application of OpenZL: <strong>compressing time-series data, especially for Internet of Things (IoT) applications.</strong> Imagine thousands, even millions, of sensors constantly reporting data – temperature, humidity, pressure, location. This generates an enormous volume of information, often repetitive and highly structured. Efficiently storing and transmitting this data is a monumental challenge, and that&rsquo;s where OpenZL shines.</p>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-time-series-compression/Mon, 26 Jan 2026 00:00:00 +0000https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-time-series-compression/<h2 id="chapter-15-project-compressing-time-series-sensor-data">Chapter 15: Project: Compressing Time-Series Sensor Data</h2> <p>Welcome to Chapter 15! This is where we bring everything we&rsquo;ve learned about OpenZL together into an exciting, hands-on project. In the real world, data is often structured, and one of the most common forms is time-series data, particularly from sensors. Think about temperature readings, IoT device metrics, or stock prices – they all have a timestamp and one or more associated values.</p>