The ARIMA tool estimates a time series forecasting model, either as a univariate model or one with covariates (predictors), using an autoregressive integrated moving average (or ARIMA) method. ARIMA is the most commonly used forecasting approach, and is considered to be the most general class of models for forecasting a time series field. The ARIMA methods implemented in this tool can use an automated approach to develop a model based on statistical criteria, or the user can directly specify the underlying parameters of an ARIMA model. A detailed discussion of the ARIMA model, along with a description of the automated methods used in this tool, can be found in Chapter 8 of Hyndman and Athanasopoulos's online book Forecasting: Principals and Practice.*

This tool uses the R programming language. Go to Options > Download Predictive Tools to install R and the packages used by the R Tool.


An Alteryx data stream containing historical data on the time series to be forecast and (optionally) a set of covariates. Fields that will not be used in model creation can also be present in the data stream.

Configuration Properties

Required parameters

Model customization (optional)

Other options

Graphics Options


  1. O Output: Consists of an output stream containing the ARIMA model object that can be used for both point forecasts and a user specified percentile confidence interval surrounding those forecasts.
  2. R Output: Consists of the report snippets generated by the ARIMA tool: a statistical summary, autocorrelation diagnostic plots and forecast plots.
  3. I Output: An interactive html dashboard consisting of plots and metrics. You can interact with the visualizations by clicking on the different graphical elements to reveal more information, values, metrics and analytics.

*Hyndman, R.J. and Athanasopoulos, G. (2012) Forecasting: Principles and Practice.