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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