Earticle

초록

영어

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

목차

Abstract
 1. Introduction
 2. Existing Method
  2.1 Regression model first and ARIMA second prediction (Regression-ARIMA)
  2.2 Time series model first and regression second prediction (ARIMA- Regression)
  2.3 Neural network prediction
 3. Proposed Method
  3.1 Construction of mixed prediction model
  3.2 Parameter estimation by genetic algorithm
 4. Test Signal
  4.1 Virtual signal
  4.2 Pore water pressure in actual dam
 5. Experimental Results
  5.1 Experimental result of virtual signal prediction
  5.2 Experimental result of actual signal prediction
  5.3 Outlier detection result of actual sensor signal
 6. Discussion and Conclusion
 References

키워드

저자

• Chang-Mok Park [ Department of Industrial Management Engineering, INDUK University, Korea ] Corresponding author

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간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380