發信人: GzLi (笑梨), 信區: DataMining
標  題: time series phd thesis
發信站: 南京大學小百合站 (Sun Dec 15 22:14:54 2002)


TIME SERIES DATA MINING: IDENTIFYING TEMPORAL
PATTERNS FOR CHARACTERIZATION AND
PREDICTION OF TIME SERIES EVENTS

Richard J. Povinelli, B.A., B.S., M.S.
A Dissertation submitted to the Faculty of the Graduate School,
Marquette University, in Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy
Milwaukee, Wisconsin
December, 1999

ABSTRACT
A new framework for analyzing time series data called Time Series Data Mining
(TSDM) is introduced. This framework adapts and innovates data mining concept
s to
analyzing time series data. In particular, it creates a set of methods that r
eveal hidden
temporal patterns that are characteristic and predictive of time series event
s. Traditional
time series analysis methods are limited by the requirement of stationarity o
f the time
series and normality and independence of the residuals. Because they attempt to
characterize and predict all time series observations, traditional time serie
s analysis
methods are unable to identify complex (nonperiodic, nonlinear, irregular, an
d chaotic)
characteristics. TSDM methods overcome limitations of traditional time series
 analysis
techniques. A brief historical review of related fields, including a discussi
on 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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