Detection of Outbreaks from Time Series Data Using Wavelet Transform

  title={Detection of Outbreaks from Time Series Data Using Wavelet Transform},
  author={Jun Zhang and Fu-Chiang Tsui and Michael M. Wagner and William R. Hogan},
  journal={AMIA ... Annual Symposium proceedings. AMIA Symposium},
In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely, negative singularity and long-term trends, which may degrade the performance of current approaches to outbreak detection. To test this approach, we introduced artificail disease outbreaks and negative singularities into a real world dataset and applied it and two other algorithms-autoregressive (AR) and… CONTINUE READING


Publications citing this paper.
Showing 1-10 of 39 extracted citations

Anomaly detection and diagnosis in grid environments

Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07) • 2007
View 3 Excerpts
Highly Influenced

Time series contextual anomaly detection for detecting market manipulation in stock market

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) • 2015
View 3 Excerpts


Publications referenced by this paper.
Showing 1-10 of 13 references

Using temporal context to improve biosurveillance.

Proceedings of the National Academy of Sciences of the United States of America • 2003
View 3 Excerpts

A study of the average run length characteristics of the national notifiable disease surveillance system

Ben Y. Reis, Kenneth D. Mandl

Time series prediction using a multiresolution dynamic predictor

Fu-Chiang Tsui
Ph.D. dissertation, • 1996
View 1 Excerpt

Similar Papers

Loading similar papers…