Auto ARIMA modeling with healthyR.ts

Time series, just like any other set of data, can be modeled. The methods are vast, both old and new. In this section, we are going to discuss ARIMA modeling, and more specifically building an automatic ARIMA model with the healthyR.ts library in R. ARIMA models themselves attempt to describe the autocorrelations in the data.

In this section, we will use a workflow that ends with the ts_auto_arima() function creating and fitting a tuned model. This model requires our data to be in tibble format. So, to do this, we will use the AirPassengers dataset and make sure it is a tibble.

Let’s get started with the dataset we have already brought in and coerce it into a tibble:

library(healthyR.ts)
library(dplyr)
library(timetk)
ap_tbl <- ts_to_tbl(ap_ts) |>
  select(-index)
> class(ap_tbl)
[1] "tbl_df"      "tbl"          ...