Ph.D. dissertation: Time Series Data Mining: Identifying Temporal Patterns for Characterization and

Richard J. Povinelli (Richard.Povinelli@mu.edu)
Wed, 29 Dec 1999 22:36:04 +0100 (MET)

Greetings,

My Ph.D. dissertation is available for download from:
http://povinelli.eece.mu.edu/publications. The title and abstract are found
below.

Best regards,
Richard

Time Series Data Mining: Identifying Temporal Patterns for Characterization
and Prediction of Time Series Events
Richard J. Povinelli
Department of Electrical and Computer Engineering, Marquette University
Milwaukee, Wisconsin

Abstract:
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.

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