I'm SUPER EXCITED to show fellow time-series enthusiasts a new way that we can scale time series analysis using an amazing technology called Spark! Without Spark, large-scale forecasting projects of 10,000 time series can take days to run because of long-running for-loops and the need to test many models on each time series. Spark has been widely accepted as a "big data" solution, and we'll use it to scale-out (distribute) our time series analysis to Spark Clusters, and run our analysis in parallel. Spark is an amazing technology for processing large-scale data science workloads. Modeltime is a state-of-the-art forecasting library that I personally developed for "Tidy Forecasting" in R. Modeltime now integrates a Spark Backend with capability of forecasting 10,000 time series using distributed Spark Clusters.

I'm beyond excited to introduce modeltime, a new time series forecasting package designed to speed up model evaluation, selection, and forecasting. Follow the updated modeltime article to get started with modeltime. If you like what you see, I have an Advanced Time Series Course where you will become the time-series expert for your organization by learning modeltime and timetk. This article is part of a series of software announcements on the Modeltime Forecasting Ecosystem. Register to stay in the know on new cutting-edge R software like modeltime.

This tutorial (view the original article here) introduces our new R Package, Modeltime H2O. If you like what you see, I have an Advanced Time Series Course where you will learn the foundations of the growing Modeltime Ecosystem. This article is part of a series of software announcements on the Modeltime Forecasting Ecosystem. Register to stay in the know on new cutting-edge R software like modeltime. Modeltime H2O is part of a growing ecosystem of Modeltime forecasting packages.