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Classification of Precipitation Data Based on Smoothed Periodogram
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 Title & Authors
Classification of Precipitation Data Based on Smoothed Periodogram
Park, Man-Sik; Kim, Hee-Young;
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It is well known that spectral density function determines auto-covariance function of stationary time-series data and smoothed periodogram is a consistent estimator of spectral density function. Recently, Kim and Park (2007) showed that smoothed- periodogram based distances performs very well for the classification. In this paper, we introduce classification methods with smoothed periodogram and apply the approaches to the monthly precipitation measurements obtained from January, 1987 through December, 2007 at 22 locations in South Korea.
Periodogram;smoothing;spectral density;clustering;precipitation;
 Cited by
Classification of Time-Series Data Based on Several Lag Windows,Kim, Hee-Young;Park, Man-Sik;

Communications for Statistical Applications and Methods, 2010. vol.17. 3, pp.377-390 crossref(new window)
공간 극단값의 분계점 모형 사례 연구 - 한국 여름철 강수량,황승용;최혜미;

응용통계연구, 2014. vol.27. 4, pp.655-665 crossref(new window)
Categorical time series clustering: Case study of Korean pro-baseball data, Journal of the Korean Data and Information Science Society, 2016, 27, 3, 621  crossref(new windwow)
Threshold Modelling of Spatial Extremes - Summer Rainfall of Korea, Korean Journal of Applied Statistics, 2014, 27, 4, 655  crossref(new windwow)
고정웅, 백희정, 권원태 (2005) 한반도 우기의 강수 특성과 지역 구분, Asia-Pacific Journal of Atmospheric Sciences, 41, 101-114

김성렬, 양진석 (1995) 한국의 온대 저기암성 강수지역 구분, <한국지역지리학회지>, 1, 45-60

김희영 , 박만식 (2007). Clustering time-series based on frequency domain, <한국통계학회 추계학술발표회 논문집>,73.

문영수 (1990). 클러스터분석에 의한 한국의 강수지역 구분, Asia-Pacific Journal of Atmospheric Sciences, 26, 203-215

이동규, 박정균 (1999) 군집 분석을 이용한 남한의 여름철 강수지역구분, Asia-Pacific Journal of Atmospheric Sciences, 35, 511-518

이승호 (1993) 계량적 분석에 의한 한국의 강수지역구분, <지역과 환경>, 11, 1-15

Bartlett, M. S. (1946). On the theoretical specification and sampling properties of auto- correlated time series, Supplement to the Journal of the Royal Statistical Society, 8, 27-41 crossref(new window)

Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Springer- Verlag, New York

Caiado, J., Crato, N. and Pena, D. (2006). A periodogram-based metric for time series classification, Computational Statistics & Data Analysis, 50, 2668-2684 crossref(new window)

Corduas, M. and Piccolo, D. (2008). Time series clustering and classification by the autoregressive metric, Computational Statistics and Data Analysis, 52, 1860-1872 crossref(new window)

Fu, T. C., Chung, F. L., Ng, V. and Luk, R. (2001). Pattern discovery from stock time series using self-organizing maps, KDD 2001 Workshop on Temporal Data Mining, August 26-29, San Francisco, 27-37

Galeano, P. and Pena, D. (2000). Multivariate analysis in vector time series, Resenhas, 4, 383-403

Goldstein, D. R., Ghosh, D. and Conlon, E. M. (2002). Statistical issues in the clustering of gene expression data, Statistica Sinica, 12, 219-240

Kakizawa, Y., Shumway, R. H. and Taniguchi, M. (1998). Discrimination and clustering for multivariate time series, Journal of the American Statstical Association, 93, 328-340 crossref(new window)

Kalpakis, K., Gada, D. and Puttagunta, V. (2001). Distance measures for effective clustering of ARIMA time series, In Proceedings of the 2001 IEEE international conference on data mining, 273-280

Liao, T. W. (2005). Clustering of time series data-a survey, Pattern Recognition, 38, 1857-1874 crossref(new window)

Maharaj, E. A. (2000). Clustering of time series, Journal of Classification, 17, 297-314 crossref(new window)

Pattarin, F., Paterlini, S. and Minerva, T. (2004). Clustering financial time series: An application to mutual funds style analysis, Computational Statistics & Data Analysis, 47, 353-372 crossref(new window)

Piccolo, D. (1990). A distance measure for classifying ARIMA models, Journal of Time Series Analysis, 11, 153-164

Shumway, R. H. (2003). Time-frequency clustering and discriminant analysis, Statistics & Probability Letters, 63, 307-314 crossref(new window)