Trend detection and data mining via wavelet and Hilbert-Huang transforms

Murat Yasar, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


This paper presents the formulation and evaluation of effective algorithms of reliable data analysis for real-time monitoring of incipient faults and anomalies, data fusion and event classification. The objective is to alleviate the shortcomings of the existing techniques for data mining by taking advantage of nonlinear filtering to handle non-Gaussian and non-stationary multiplicative noise and uncertainties. New concepts have been developed toward characterization of the data features and behavior interpretation of the underlying processes to evaluate their performance. In particular, the techniques of wavelet transform, Hilbert-Huang transform, and symbolic encoding are investigated to explore their effectiveness and relative simplicity to interpret and implement data mining tasks.

Original languageEnglish (US)
Title of host publication2008 American Control Conference, ACC
Number of pages6
StatePublished - 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2008 American Control Conference, ACC
Country/TerritoryUnited States
CitySeattle, WA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


Dive into the research topics of 'Trend detection and data mining via wavelet and Hilbert-Huang transforms'. Together they form a unique fingerprint.

Cite this