Browse > Article
http://dx.doi.org/10.7846/JKOSMEE.2012.15.2.156

Outlier Detection of the Coastal Water Temperature Monitoring Data Using the Approximate and Detail Components  

Cho, Hong-Yeon (Marine Environment & Conservation Research Department, KORDI)
Oh, Ji-Hee (Marine Environment & Conservation Research Department, KORDI)
Publication Information
Journal of the Korean Society for Marine Environment & Energy / v.15, no.2, 2012 , pp. 156-162 More about this Journal
Abstract
Outlier detection and treatment process is highly required as the first step for the statistical analysis of the monitoring data having many outliers frequently occurred in the coastal environmental monitoring projects. In this study, the outlier detection method using the approximate and detail (or residual) components of the (raw) data is suggested. The approximate and detail components of the data can be separated by the diverse filtering and smoothing methods. The decomposition of the data is carried out by the harmonic analysis and local regression curve, respectively. Then, the Grubbs' test and modified z-score method widely used to detect outliers in the data are applied to the detail components of the water temperature data. The new data set is reconstructed after removed the outliers detected by these methods. It can be shown that the suggested process is successfully applied to the outlier detection of the coastal water temperature monitoring data provided by the Real-time Information System for Aquaculture Environment, National Fisheries Research and Development Institute (NFRDI).
Keywords
outlier; approximations and details; water temperature monitoring data; Grubbs test; modified z-score method; residual;
Citations & Related Records
연도 인용수 순위
  • Reference
1 국립수산과학원, 2012, 실시간 어장정보시스템. http://portal.nfrdi.re.kr/risa/.
2 Agresti, A. and Franklin, C., 2007, Statistics, The Art and Science of Learning from Data, Pearson Education, Inc. pp.693.
3 Barnett, V. and Lewis, T., 1994, Outliers in Statistical Data, Third Edition, John Wiley & Sons, Ltd., Chichester, UK, pp.584.
4 Cho, H.Y., Suzuki, K. and Nakamura, Y., 2010, Hysteresis loop model for the estimation of the coastal water temperatures, -by using the buoy monitoring data in Mikawa Bay, Japan-, Report of the Port and Airport Research Institute, 49(2), pp.123-153.
5 Dixon, W.J., 1950, Analysis of Extreme Values, The Annals of Mathematical Statistics, 21(4), pp.488-506.   DOI   ScienceOn
6 Garcia, F.A.A., 2010, Tests to identify outliers in data series, http://www.se.mathworks.com/matlabcentral/fileexchange/28501, MATLAB Central File Exchange. Retrieved January 19th, 2012.
7 Grubbs, F.E., 1950, Sample Criteria for Testing Outlying Observations, The Annals of Mathematical Statistics, 21(1), pp.27-58.   DOI   ScienceOn
8 Hair, J.F. Jr., Black, W.C., Babin, B.J. and Anderson, R.E., 2010, Multivariate Data Analysis, A Global Perspective, Seventh Edition, Chapter 2, Pearson Education, Inc., New Jersey, USA, pp.800.
9 Martinez, W.L. and Martinez, A.R., 2005, Exploratory Data Analysis with MATLAB, Computer Science and Data Analysis Series, Chapman & Hall/CRC. pp.405.
10 Rousseeuw, P.J. and Leroy, A.M., 2003, Robust Regression and Outlier Detection, John Wiley & Sons. pp.329.