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Activity Number:
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146
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #310026 |
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Title:
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Feature Extraction Using Functional Data Analysis for Screening Large Numbers of Time Series
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Author(s):
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Ganesh Subramaniam*+ and Ravi Varadhan and Ciprian M. Crainiceanu
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Companies:
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AT&T Labs - Research and Johns Hopkins University and Johns Hopkins University
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Address:
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180 Park avenue, Florham Park, NJ, 07932,
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Keywords:
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Semiparametric Models ; Smoothing ; Penalized Splines ; Derivatives ; Data Mining
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Abstract:
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Business decisions and business process monitoring are often based on time series data that represent an aggregation of a large number of time series. Although the inferences are generally based on the aggregate data, significant insights lurk in the underlying time series that had been combined. The challenge that analysts currently face is the large number and complexity of data underlying aggregate time series. This paper provides a framework that supports drill down analysis and screening of large scale time series data by developing feature extraction rules. We develop an exploratory method based on functional data analysis, where we fit smooth functions with semiparametric models using penalized splines. One feature extraction involves estimating derivatives from these models. These provide insights on the bumps and dips for the underlying time series.
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