A special section, Time Series Monitoring with five papers and introduction by editors Wilpen Gorr and Keith Ord, appears in the July-September 2009 issue of the International Journal of Forecasting. The papers take a fresh look at this field with new societal applications and new methods and frameworks.

Following are excerpts from the introduction paper...

“Screening products, populations, or territories for exceptional changes in demand for products or services is an important management activity, whether for the prevention of losses or to take advantage of opportunities. In either event, managers must make decisions that interrupt normal operations and reallocate resources. To trigger such activity, time series monitoring has the purpose of automatically detecting outliers and structural changes, such as step increases or decreases, in time series data as soon as possible after they occur and with sufficiently few false alarms.”

“What is new now? One development is the application of time series monitoring to social issues, such as communicable disease detection and crime prevention. There is even less ability to control associated processes than with product demand and there is the added richness of spatial patterns becoming prominent features of exceptional behavior. In addition, the costs and benefits of monitoring need to be addressed as a matter of public policy. In response, this special section provides new theoretical results on forecasting and monitoring, new monitoring methods that include spatial components and take advantage of advances in computer science (spatial scan statistic), the application of an evaluative framework that is non-parametric and includes explicit and practical methods for achieving an optimum cost/benefit balance (receiver operating characteristic curves), and new estimation methods estimation methods that better accommodate non-stationarities (state space framework).”

 

1.

Introduction to time series monitoring
Pages 463-466
Wilpen L. Gorr, J. Keith Ord


 

2.

How does improved forecasting benefit detection? An application to biosurveillance
Pages 467-483
Thomas H. Lotze, Galit Shmueli

 


 

3.

Empirical calibration of time series monitoring methods using receiver operating characteristic curves
Pages 484-497
Jacqueline Cohen, Samuel Garman, Wilpen Gorr


 

4.

Expectation-based scan statistics for monitoring spatial time series data
Pages 498-517
Daniel B. Neill

 


 

5.

Monitoring processes with changing variances
Pages 518-525
J. Keith Ord, Anne B. Koehler, Ralph D. Snyder, Rob J. Hyndman

 


 

6.

Incorporating a tracking signal into a state space model
Pages 526-530
Ralph D. Snyder, Anne B. Koehler

The International Institute of Forecasters, in collaboration with SAS®, has announced the SAS Grants to Support Research on Forecasting for 2009. This will be the seventh year that the Grants have been awarded. There are two Grants of $5,000. See the Researchers Page for more information. See also on the Researchers Page a document listing research needs for forecasting in order to help you identify research topics that are most likely to produce useful findings.

Andreas Graefe developed a tutorial for the site's Delphi freeware in order to brief participants in an experiment. The tutorial is in the form of a PowerPoint show with a voiceover. We think the tutorial will be useful for all users of the software, and we have made it available on the Software Page (see the menu bar at left).

The program schedule for the International Symposium on Forecasting, to be held in Hong Kong on 21-24 June, is now available from the conference web pages.

"Rethinking the Ways We Forecast" is the feature section in the upcoming Summer 2009 issue of FORESIGHT. David Orrell and Patrick McSharry explain why we’ve seen huge breakdowns in our models when we try to forecast complex systems, such as the economy, the weather, and our genetic network. Needed, they argue, are 1) new methodologies that explicitly address key features of complex systems, and 2) a shift of emphasis away from single-point forecasts and toward scenarios of possible futures.

Commentaries from Roy Batchelor and from Paul Goodwin and Robert Fildes punch back, saying, in so many words, “Not so fast -- the new tools haven’t proven themselves and may complexify without improving our forecasts.”

Also in this issue of FORESIGHT:

  • Roy Pearson shows where to find Free and Easy Access to Monthly Forecasts.
  • Jim Hoover shows How to Track Forecast Accuracy to Guide Forecast Process Improvement.
  • Analysts at Hewlett-Packard show how to combine time series, regression, and life-cycle models for Forecasting Demand for Spare Parts.
  • A new column on Sales and Operations Planning, which is perhaps the most significant innovation of this generation in promoting collaborative forecasting.
  • Peter Sephton’s book review of Leonard Mlodinow’s The Drunkard’s Walk: How Randomness Rules Our Lives. “If we can better understand the role of randomness in bringing us to where we are now, we can possibly achieve better mastery over our future.”

For more information about Foresight, go to www.forecasters.org/foresight.