References
- 1. Iglewicz, B., Hoaglin, D.C.: How to detect and handle outliers. Milwaukee, WI.: ASQC Quality Press, 1993.
- 2. Sun S. Z., LI, H., Sun, H. : Measurement and analysis of coastal waves along the north sea area of China. Polish Maritime Research, 3 (91) 2016, 23, pp. 72-78.
- 3. Whan Lee, J., Park, S. C., Kee Lee, D., Ho Lee, J. : Tsunami arrival time detection system applicable to discontinuous time-series data with outliers. Journal of natural hazards and earth sciences, 2016, 16 (12), pp. 2603-2016.
- 4. Mínguez, R., Reguero, B.G., Luceño, A., Méndez, F.J. : Regression models for outlier identification (hurricanes and typhoons) in wave hindcast databases. Journal of Atmospheric and Oceanic Technology, 2012, 29, pp. 267–285.10.1175/JTECH-D-11-00059.1
- 5. Lucas, C., Muraleedharan, G., Soares, C. G. : Outliers identification in a wave hindcast dataset used for regional frequency analysis. Maritime Technology and Engineering, 2015, pp. 1317-1327.
- 6. Reguero, B.G., Menéndez, M., Méndez, F.J., Mínguez, R., Losada, I. J. : A Global Ocean Wave (GOW) calibrated reanalysis from 1948 onwards. Coastal Engineering, 2012, 65, pp. 38–55.
- 7. Chandola. V., Banerjee, A., Kumar, V. : Anomaly detection – a survey. ACM Comput Surv. 2009, 4 (3), pp. 1–58.10.1145/1541880.1541882
- 8. Barnett, V., Lewis, T. : Outliers in Statistical Data. John Wiley, 3rd edition 1994.
- 9. Zhang, Ji. : Advancements of Outlier Detection: A Survey. ICST Transactions on Scalable Information Systems, 2013, 13 (1), pp. 1-26.
- 10. Muraleedharan, G., Lucas, C., Guedes Soares, C.: Regression quantile models for estimating trends in extreme significant wave heights. J. Ocean Engineering. 2016, 118, pp. 204–215.
- 11. Zhang, K., Hutter, M., Jin, H. : A new local distance-based outlier detection approach for scattered real-world data. Proc. 13th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 2009, pp. 813-822.
- 12. Chen, Y., Miao, D., Zhang, H. : Neighborhood outlier detection. Expert Systems with Applications, 2010, 37 (12), pp. 8745-8749.
- 13. Breunig, M. M., Kriegel, H.-P., Ng, R. T., et al.: LOF: Identifying density-based local outliers. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), Proceedings of the ACM SIGMOD international conference on management of data, ACM Press, Dallas, Texas, 2000, pp. 93–104.
- 14. Troncoso, A., Salcedo-Sanz, Casanova-Mateo, S., Riquelme, J.C, C., Prieto, L. : Local models-based regression trees for very short-term wind speed prediction. Renewable Energy, 2015, 81, pp. 589-598.