원문정보
한국인터넷방송통신학회
The International Journal of Advanced Smart Convergence
Volume 7 Number 1
2018.03
pp.24-32
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보