FULL TUTORIAL: Build a Full Production Forecasting Workflow in R with Targets & Modeltime

This is a FULL TUTORIAL that has 2 Parts. First, we interview Special Guest: Will Landau, Creator of Targets! Then we do an insane forecasting lab implementing #modeltime & #targets to: Make 32 Time Series Models (18 ARIMA, 18 Prophet) Track Accuracy for every model/time series combination Select best models for each of 18 time series Automates a Forecast Audit with Error Reporting WANT THE CODE? Join Learning Labs PRO: https://university.business-science.i... WANT TO LEARN TIME SERIES FORECASTING? Join my Time Series Course: https://university.business-science.i... TABLE OF CONTENTS: 00:00 Energy Forecasting: Modeltime & Targets 00:50 Goals for Today: Targets for Production Forecasting 02:35 Workflow: Modeltime - Targets - Rmarkdown 04:52 Interview with Will Landau, Creator of Targets 05:35 In Grad School, PhD Work: Models with Long Runtimes 07:02 Identified a Gap in R Ecosystem: No Pipeline Tools 07:37 End of Grad School began developing Drake (& then Targets) 09:25 Why working on Targets gives Will joy 09:40 R Community: How it's benefited Will's R Package Development 10:10 ROpenSci: Access to the best developers in R community 10:46 Kirill Mueller's Influence: Proposing High-Performance Computing 11:40 What is ROpenSci? 12:59 Matt & Will's Shared Experience with R Community 14:29 Will's Bayesian & Statistics Background 15:00 Iowa State: BioTech NextGen DNA Sequencing Data Analysis Group 16:12 Genomics Project: GPU Computing, Hierarchical Models, & Genomics Data 16:47 Modeling Crop Yield with Genomics (Massive Models) 17:30 STAN & JAGS Models were too computationally expensive 18:06 Creating a Markov Chain Monte Carlo (MCMC) Simulation using GPUs 18:44 Massive Speed Gains: Turned Days (CPU) to 4 Hours (GPU) 19:15 Will wishes he had Targets: Instant Parallelization 20:20 Parallel Computing is Simple in Targets 21:30 FREE RESOURCE: Targets Book https://books.ropensci.org/targets/ 23:07 Business Problem: Scalable Time Series Modeling with ARIMA & Prophet 26:21 Forecast Audit Report (Data Product) 28:12 Why Targets? 32:00 Key Concept: Branching https://books.ropensci.org/targets/dy... 34:00 Tarchetypes: Targets Ecosystem Expansion https://docs.ropensci.org/tarchetypes/ 36:30 Code Demo: Targets + Modeltime 36:48 Targets Workflow for Energy Forecast Reporting 38:04 Project Setup 39:45 Module 01: Targets Branching Basics 43:01 Branching with tarchetypes::tar_group_by() 48:38 Dynamic Modeling: 15 Linear Regresions by Auto Manufacturer 53:17 Broom Tidiers: Getting Coefficient & Accuracy Metrics for 15 LM Models 57:50 Module 02: Time Series Forecasting with Modeltime + Targets 1:01:30 Data Import & Preparation Targets 1:06:03 Clean Energy Data Target 1:07:37 Extend Energy Data Target 1:10:40 Branching to 18 Time Series with tarchetypes::tar_group_by() 1:13:46 Time Series Splitting 1:15:41 Making 36 Time Series Models: 18 ARIMA & 18 Prophet 1:18:37 LL PRO Challenge: Add a GLMNet Model 1:18:56 Test Set Accuracy & Model Comparison 1:23:13 Model Selection (Lowest RMSE) 1:25:08 Model Refitting 1:26:58 Final Forecast (Future Data) 1:29:37 Forecast Audit (Accuracy Checking) 1:32:56 Automated Report 1:34:53 Learning More: 5-Course R-Track Program https://university.business-science.i... 1:41:14 Q&A