Traditional forecasting methods require stationary data to make a valid forecast. In this post I’ll run through a relatively simple time-series forecast that employs forcast package’s auto.arima function and regression tress to via the rpart library. library(RCurl) library(forecast) library(lubridate) library(tidyverse) library(ggforce) library(kableExtra) library(rpart) library(rpart.plot) library(data.table) Here’s a preview of the data I’m working with. It’s the same EMS data (with different aggregations) from my EMS Flexdashboard post. https://jbrnbrg.