Abstract/Details

Time series data mining: Identifying temporal patterns for characterization and prediction of time series events


1999 1999

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Abstract (summary)

A new framework for analyzing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovates data mining concepts to analyzing time series data. In particular, it creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Traditional time series analysis methods are limited by the requirement of stationarity of the time series and normality and independence of the residuals. Because they attempt to characterize and predict all time series observations, traditional time series analysis methods are unable to identify complex (nonperiodic, nonlinear, irregular, and chaotic) characteristics. TSDM methods overcome limitations of traditional time series analysis techniques. A brief historical review of related fields, including a discussion of the theoretical underpinnings for the TSDM framework, is made. The TSDM framework, concepts, and methods are explained in detail and applied to real-world time series from the engineering and financial domains.

Indexing (details)


Subject
Electrical engineering;
Computer science;
Mathematics
Classification
0544: Electrical engineering
0984: Computer science
0405: Mathematics
Identifier / keyword
Applied sciences; Pure sciences; Data mining; Time series
Title
Time series data mining: Identifying temporal patterns for characterization and prediction of time series events
Author
Povinelli, Richard James
Number of pages
180
Publication year
1999
Degree date
1999
School code
0116
Source
DAI-B 60/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780599564657, 0599564652
Advisor
Feng, Xin
University/institution
Marquette University
University location
United States -- Wisconsin
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
9953495
ProQuest document ID
304514506
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304514506
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