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Related Experiment Videos

Automated time series forecasting for biosurveillance.

Howard S Burkom1, Sean Patrick Murphy, Galit Shmueli

  • 1The Johns Hopkins University Applied Physics Laboratory, MD, USA. howard.burkom@jhuapl.edu

Statistics in Medicine
|March 6, 2007
PubMed
Summary

The Holt-Winters method best predicted syndromic data trends for biosurveillance, outperforming regression models. This improves anomaly detection by removing systematic data behavior for more robust monitoring.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Time Series Analysis

Background:

  • Biosurveillance relies on control charts for anomaly detection.
  • Traditional methods require data free of trends and systematic patterns.
  • Time series forecasting can preprocess data by removing systematic behavior.

Purpose of the Study:

  • To compare the predictive accuracy of three time series forecasting methods for biosurveillance.
  • To evaluate the effectiveness of these methods in removing systematic behavior from syndromic data.

Main Methods:

  • Three forecasting methods were compared: non-adaptive regression, adaptive regression, and Holt-Winters exponential smoothing.
  • Predictive accuracy was assessed using root-mean-square error, median absolute per cent error (MedAPE), and median absolute deviation.
  • The methods were applied to 16 authentic syndromic data streams without individual series tuning.

Main Results:

  • The Holt-Winters method demonstrated superior performance based on median-based error metrics (MedAPE averaged 9.7).
  • Adaptive regression (MedAPE 11.6) outperformed non-adaptive regression (MedAPE 16.5), which was sensitive to baseline data changes.
  • Holt-Winters was most effective in reducing serial autocorrelation, with most 1-day-lag coefficients below 0.15.

Conclusions:

  • The Holt-Winters method is recommended for preprocessing biosurveillance data due to its accuracy and ability to handle data complexities.
  • Tuning forecasting models to individual series can further enhance prediction, but requires reliable data classification for practical implementation.