aRima.helper

Simplifies ARIMA modeling by providing a user interface and immediate results. User can specify non-seasonal and/or seasonal AR, MA, and difference terms as well as dummy & Fourier terms. Stationarity test, model convergence, fit statistics, ACF & PACF plots, residual plots, and white noise plots all provided. This package heavily uses the ‘forecast’ package by Rob Hyndman.

Install

To install from github

install.packages("devtools")
devtools::install_github("kevcraig/aRima.helper")

To load once installed

library(aRima.helper)

Usage

By default, the aRima_helper function will return a local shiny app based on the time-series provided.

Non-seasonal time-series

The most simple case. If a time-series object with no frequency is provided, a non-seasonal shiny app will be returned with inputs for AR, MA, and difference terms.

Example with WWWusage from datasets

library(aRima.helper)
aRima_helper(WWWusage)

Seasonal time-series

If a time-series object with a frequency is provided, a seasonal shiny app will be returned with inputs for AR, MA, difference, seasonal AR, seasonal MA, and seasonal difference terms. You can also specify special terms such as dummies or Fourier. The amount of Fourier terms used is equal to frequency/2.

Example with AirPassengers from datasets

library(aRima.helper)
aRima_helper(AirPassengers)

Analyze

The function returns a series of outputs that refresh with every input change. These include:

• Model convergence
• Stationarity (assessed on the time-series with forecast::ndiffs() for non-seasonal and forecast::nsdiffs() for seasonal models)
• Fit statistics - AIC, BIC, RMSE, MAE, MAPE
• Actual vs. fitted plot
• ACF plot
• PACF plot
• White noise plot (based on Ljung–Box test where degrees of freedom equals the sum of seasonal and non-seasonal AR & MA terms included)