1 |
Priestley, M. B. (1981). Spectral Analysis and Time Series, Academic Press, New York.
|
2 |
R Development Core Team (2006). R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
|
3 |
Shumway, R. H. (2003). Time-frequency clustering and discriminant analysis, Statistics and Probability Letters, 63, 307-314.
DOI
ScienceOn
|
4 |
Wismuller, A., Lange, O., Dersch, D. R., Leinsinger, G. L., Hahn, K., Putz, B. and Auer, D. (2002). Cluster analysis of biomedical image time-series, International Journal of Computer Vision, 46, 103-128.
DOI
|
5 |
Caiado, J., Crato, N. and Pena, D. (2006). A periodogram-based metric for time series classification, Computational Statistics and Data Analysis, 50, 2668-2684.
DOI
ScienceOn
|
6 |
Chatfield, C. (1975). The Analysis of Time Series: Theory and practice, Chapman & Hall, London.
|
7 |
Chen, G., Abraham, B. and Peiris, S. (1994). Lag window estimation of the degree of differencing in fractionally integrated time series models, Journal of Time Series Analysis, 15, 473-487.
DOI
|
8 |
Corduas, M. and Piccolo, D. (2008). Time series clustering and classification by the autoregressive metric, Computational Statistics and Data Analysis, 52, 1860-1872.
DOI
ScienceOn
|
9 |
Cowpertwait, P. S. P. and Cox, T. F. (1992). Clustering population means under heterogeneity of variance with an application to a rainfall time series problem, The Statistician, 41, 113-121.
DOI
ScienceOn
|
10 |
Galeano, P. and Pena, D. (2000). Multivariate analysis in vector time series, Resenhas, 4, 383-403.
|
11 |
Golay, X., Kollias, S., Stoll, G., Meier, D., Valvanis, A. and Boesiger, P. (1998). A new correlation-based fuzzy logic clustering algorithm for fMRI, Magnetic Resonance in Medicine, 40, 249-260.
DOI
|
12 |
Goutte, C., Toft, P., Rostrup, E., Nielsen, F. A. and Hansen, L. K. (1999). On clustering fMRI time series, Neuroimage, 9, 298-310.
DOI
ScienceOn
|
13 |
Kakizawa, Y., Shumway, R. H. and Taniguchi, M. (1998). Discrimination and clustering for multivariate time series, Journal of American Statstical Association, 93, 328-340.
DOI
|
14 |
Kovacic, Z. J. (1996). Classification of time series with applications to the leading indicator selection, In Proceedings of the Fifth Conference of IFCS, 2, 204-207.
|
15 |
Kullback, S. (1978). Information Theory and Statistics, Peter Smith, Gloucester, Massachusetts.
|
16 |
Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, Springer-Verlag, New York.
|
17 |
Baker, F. B. and Hubert, L. J. (1975). Measuring the power of hierarchical cluster analysis, Journal of the American Statistical Association, 70, 31-38.
DOI
|
18 |
Bohte, Z., Cepar, D. and Kosmelij, K. (1980). Clustering of time series, In Proceedings of COMPSTAT, 587-593.
|
19 |
Brillinger, D. (1981). Time Series: Data Analysis and Theory, Holden-Day, San Francisco.
|
20 |
Kullback, S. and Leibler, R. A. (1951). On information and sufficiency, Annals of Mathematical Statistics, 22, 79-86.
DOI
|
21 |
Macchiato, M., La Rotonda, L., Lapenna, V. and Ragosta, M. (1995). Time modelling and spatial clustering of daily ambient temperature an application in Southern Italy, Environmetrics, 6, 31-53.
DOI
|
22 |
Maharaj, E. A. (2000). Cluster of time series, Journal of Classification, 17, 297-314.
DOI
|
23 |
Park, M. S. and Kim, H.-Y. (2008). Classification of precipitation data based on smoothed periodogram, The Korean Journal of Applied Statistics, 21, 547-560.
과학기술학회마을
DOI
ScienceOn
|
24 |
Pena, D. and Poncela, P. (2006). Nonstationary dynamic factor models, Journal of Statistical Planning and Inference, 136, 1237-1257.
DOI
ScienceOn
|
25 |
Piccolo, D. (1990). A distance measure for classifying ARIMA models, Journal of Time Series Analysis, 11, 153-164.
DOI
|