센서학회지 (Journal of Sensor Science and Technology)

  • 제15권6호
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  • Pages.433-441
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  • 2006
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  • 1225-5475(pISSN)
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  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
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주기 패턴을 이용한 센서 네트워크 데이터의 이상치 예측

Outlier prediction in sensor network data using periodic pattern

  • 김형일 (동국대학교 컴퓨터공학과)
  • Kim, Hyung-Il (Dept. of Computer Engineering, Dongguk University)
  • 발행 : 2006.11.30
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초록

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.

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참고문헌

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