JSM 2005 - Toronto

Abstract #302789

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 200
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Business and Economics Statistics Section
Abstract - #302789
Title: Outliers and Time Series Data Mining
Author(s): Lon-Mu Liu and William J. Lattyak*+ and John L. Harris
Companies: Scientific Computing Associates Corp. and University of Illinois, Chicago and Progress Energy, Inc.
Address: 1410 N. Harlem Avenue, Suite F, River Forest, IL, 60305,
Keywords: Data mining ; Time series ; Forecasting ; Outliers ; Automatic modeling ; Electricity loads
Abstract:

The modern economy has become more information-based. This has profoundly altered the environment in which businesses and other organizations operate. Hence it also has altered the way in which business operations and data are collected and analyzed. Given the widespread use of information technology, a large number of data are collected in online, real-time environments, which results in massive amounts of data. Such time-ordered data typically can be aggregated with an appropriate time interval, yielding a large volume of equally spaced time series data. This can be explored and analyzed using many useful tools and methodologies developed in modern time series analysis. Such practice, known as time series data mining, will be discussed in this paper. Automatic outlier detection and adjustment is a crucial technique employed in time series data mining. A time series consisting of four years of daily electricity loads will be used to illustrate outliers and time series data mining applications.


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Revised March 2005