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This example uses the SeriesP.jmp sample data table to show how to perform a time series analysis. You first create a new column that is appropriate for the Time ID.
1.
Select Help > Sample Data Library and open Time Series/SeriesP.jmp.
The SeriesP.jmp data table contains a Year column and a Quarter column to identify the time period during which the responses were observed. However, the Time Series platform requires one column with unique, equally spaced time points to label the X axis. If no Time ID is specified, then the row number is used to identify the time periods. To avoid this and make the report easier to interpret, you construct a Time ID column from Year and Quarter.
2.
Select Cols > New Columns. In the Column Name box, type Year.Quarter.
3.
Select Column Properties > Formula.
4.
Select Year and then click the plus sign.
5.
Select Quarter and then click the division sign. Type in 4 and press Enter.
6.
Figure 16.16 New Column
7.
1.
Select Analyze > Specialized Modeling > Time Series.
2.
Select GDP and click Y, Time Series.
3.
Select Year.Quarter and click X, Time ID.
4.
Figure 16.17 Time Series Report for SeriesP.jmp
Figure 16.18 Difference Report for SeriesP.jmp
7.
Click the Time Series GDP red triangle and select Smoothing Model > Linear Exponential Smoothing.
8.
Click Estimate.
9.
Click the Time Series GDP red triangle and select ARIMA Model Group. This enables you to fit multiple ARIMA models for a range of values of (p,d,q)(P,D,Q).
Fix d, the differencing order, at 1 by setting the range from 1 to 1 because the differencing report showed lag-1 differencing was appropriate.
Set p, the autoregressive order, to range from 0 to 1 because the original series showed evidence of autocorrelation.
Set q, the moving average order, to range from 0 to 1.
Leave P, D, and Q set at 0, since the series showed no evidence of seasonality.
Figure 16.19 ARIMA Model Group Specification
11.
Click Estimate.
Figure 16.20 Model Comparison Table
Figure 16.21 Model Report for ARIMA(0,1,0)

Help created on 3/19/2020