This tutorial shows how to run and interpret the **X-13ARIMA-SEATS procedure** using the XLSTAT-R engine in Excel.

## Dataset to fit the X-13ARIMA-SEATS procedure

An Excel sheet with both the data and the results can be downloaded by clicking on the button below:

Download the data

This dataset represents the United States unemployment level (thousands of persons) since January 1990 to November 2016, month by month. Here is the source for this dataset: U.S. Bureau of Labor Statistics, retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/LNU03000000, December 14, 2016.

## Goal of this tutorial

The goal of this tutorial is to set up an X13-ARIMA-SEATS procedure on this dataset, to identify a trend, and to predict the United States unemployment level in the future.

The X13-ARIMA-SEATS function developed in XLSTAT-R calls the *seas *function from the seasonal package in R (Mike Toews).

## Setting up an X-13ARIMA-SEATS procedure in XLSTAT-R

**XLSTAT-R/seasonal/Arima x13(seas)**Command (see below).

The

**X-13 Arima**dialog box appears.

In the

**General**tab, select column B (unemployment level) for the

**Time Serie**field. Then select column A into the

**Date data**field. The

**Period**field corresponds to the number of observations per unit of time. Here, we select

**12**, as we are working year by year, with a single observation per month. The

**Custom model parameters**allows you to fill your own model parameters. Here, we choose not to activate it thus the X-13ARIMA-SEATS procedure will automatically compute the parameters.

In the

**Options**tab, you can either check or not the options to automatically detect the outliers, compute the model on your log-transformed data, or detect trading days and Easter effects with AIC tests. You have also the possibility to choose the

**x11**procedure rather than the X-13ARIMA-SEATS. Here, we retain the X-13ARIMA-SEATS procedure for the computations.

## Interpreting the results of X-13ARIMA model

The following table represents the chosen model parameters for the procedure. Here, the X-13ARIMA-SEATS procedure retained the following model (p=1 d=1 q=2) (P=0 D=1 Q=1).

The third table displays the predictions of the initial time serie based on the fitted model.

Finally, the last table shows the forecasted values with lower and upper bounds.

A graph is also available showing the original and the corrected time serie based on the X-13 model.

You will find a tutorial which explains more in detail how to set up and interpret an ARIMA in general. Click here, to read more.