Automated Mining of Approximate Periodicity on Numeric Data: A Statistical Approach
Authors: 
Ran He, Sen Yang, Jingyuan Yang, Jin CaoAuthors Info & Claims
Ran He, Sen Yang, Jingyuan Yang, Jin CaoAuthors Info & ClaimsPages 20 - 27
Abstract
As an active subfield of Automated Machine Learning, automated structural analysis focuses on extracting the structural information, such as periodicity, from the data automatically, enabling automated data cleaning and feature extraction. Little research, however, has been done on the periodicity mining from numeric data that contain noises and missing points. In this paper, we present a practical and innovative framework to close this gap. To validate our approach, we carry out detailed simulation studies and real data analyses. The experimental results show that our framework is more robust to data granularity with better accuracy and computational efficiency when comparing with baseline methods. Moreover, the results imply that our proposed method is insensitive to data jitters, noise points and missing signal points.
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Index Terms
- Automated Mining of Approximate Periodicity on Numeric Data: A Statistical Approach
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Published In
March 2018
94 pages
ISBN:9781450363594
DOI:10.1145/3193077
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Association for Computing Machinery
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Published: 23 March 2018
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ICCDA 2018
ICCDA 2018: 2018 The 2nd International Conference on Compute and Data Analysis
March 23 - 25, 2018
IL, DeKalb, USA
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