1. Frédéric Picard and Steve Matthews
Use of the SAS High-Performance Forecasting Software to Detect Break in Time Series
Frédéric Picard and Steve Matthews
SAS OPUS
Ottawa, Ontario
November 26, 2015

2. Outline Context SAS High Performance Forecasting Software
Exploration of a Few Options
Conclusion

3. Context
Most Time Series at Statistics Canada are Surveys Repeated Over Time
Annual Surveys
Sub-Annual Surveys (Monthlies and Quarterlies)
Several Domains

4. Methodological Changes in Time Series
Try to minimize changes to surveys, but sometimes necessary:
changing requirements
changing population characteristics (cell phones, e-questionnaire)
maintain or gain efficiency (sampling error)
Artificial change could be misinterpreted as meaningful
prefer to revise past to remove effect
Impact is best estimated by conducting parallel run (costly)

5. Use of Forecasts for Time Series Break Detection
Select a model based on past data
Use that model to “forecast” the current period along with a 95% prediction interval.
Looks if the survey estimate for the current period falls within the interval.
If the survey estimate is outside of the interval then it is an indication that there is a break.

6. Statistical Forecasting : Annual Example
Salaries and Wages, Industry domain
ARIMA (1,1,0)

7. Challenges Related to the Volume
Tens of thousands of survey estimates
Model selection and estimation
Manual model selection is a difficult and time consuming process
Automated model selection can sometimes fail
Inclusion or not of an auxiliary variable to improve forecast.

8. SAS High Performance Forecasting Features
Several Options
Automated model selection
Fast
Robust
GUI
SAS code (proc HPFdiagnose, HPFengine…)
GUI can generate the corresponding SAS code

9. Several Options Model selection criteria (MAPE, RMSE,…),
In-Sample vs Out-of-Sample
Inclusion or not of an auxiliary variable (force or let HPF decide)
Transform the data or not
Several models available ESM, ARIMAX, UCM, IDM
Outlier detections

10. Robust If ARIMAX models fail, HPF will try simpler models such as ESM.
It has intermittent demand models for data with a lot of zeroes
HPF will (almost) always provide a forecast and a confidence interval if the syntax and file formats are correct.

11. For our Project
Used GUI to explore the different options and their impact on the models
Looked at the generated SAS code
We use the SAS code for the production
Easier to manage datasets
A little bit more flexible
Easy to reuse with other datasets or when data is updated

12. Proc HPFdiagnose and HPFengine
Helpful to determine the best model:
Does the series have a trend?
Is the series seasonal?
Is the auxiliary variable a good predictor of the variable of interest?
Should we transform the data using the log?

13. Forecasting a Seasonal Monthly Series
Energy Consumption, Province*Industry domain
ARIMA (0,1,0)(2,0,0)

14. Forecasting a non-seasonal Monthly Series
Energy Consumption, Province*Industry domain
ESM with trend

15. Usefulness of an auxiliary variable
Sometimes, we have an auxiliary variable
It has the potential of increasing the precision
Usually, in order to be useful
It has to be available for the period that we want to forecast the variable of interest
It has to be a good predictor of the variable of interest.

16. Statistical Forecasting: Annual Example
Total Revenue, Province*Industry domain
ARIMA (0,1,0)

17. Statistical Forecasting: Annual Example
Total Revenue, Province*Industry domain
ARIMA(1,1,0)
+X(1)

18. Roles to Variables

19. Corresponding Code proc hpfdiagnose data=… criterion=mape holdout=0;
transform type=auto ;
id date_SAS interval=YEAR;
forecast Revenue ;
input TaxableRevenue / REQUIRED=MAYBE(POSITIVE) ;
arimax identifyorder=both;
esm;
run;

20. Transform Data or Not

21. Corresponding Code proc hpfdiagnose data=… criterion=mape holdout=0;
transform type=auto ;
id date_SAS interval=YEAR;
forecast Revenue ;
input TaxableRevenue / REQUIRED=MAYBE(POSITIVE) ;
arimax identifyorder=both;
esm;
run;

22. Selection Criterion

23. Selection Criterion

24. Corresponding Code proc hpfdiagnose data=… criterion=mape holdout=0;
transform type=auto ;
id date_SAS interval=YEAR;
forecast Revenue ;
input TaxableRevenue / REQUIRED=MAYBE(POSITIVE) ;
arimax identifyorder=both;
esm;
run;

25. Conclusion
HPF was helpful to us to help manage the large number of forecast
HPF allowed us to use complex models (with auxiliary variables)
The GUI was helpful to explore different options
HPF is highly automated but we have the option to intervene

26. Time Series Research and Analysis Centre
Thank you!
For more information,  Pour plus d’information,
please contact: veuillez contacter :
Frederic Picard
Time Series Research and Analysis Centre
Statistics Canada