How to cite this paper
Hendrawati, T., Wigena, A., Sumertajaya, I., Sartono, B., Pravitasari, A & Asnawi, M. (2024). The ensemble distance on model-based clustering for regions clustering based on rainfall: The case of rainfall in West Java Indonesia.International Journal of Data and Network Science, 8(2), 1187-1196.
Refrences
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Anderson, D. R., & Burnham, K. P. (2002). Avoiding Pitfalls When Using Information-Theoretic Methods. The Journal of Wildlife Management, 66(3), 912. https://doi.org/10.2307/3803155
Brewer, M. J., Butler, A., & Cooksley, S. L. (2016). The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7(6), 679–692. https://doi.org/10.1111/2041-210X.12541
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Socio-logical Methods and Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644
Caiado, J., Crato, N., & Peña, D. (2006). A periodogram-based metric for time series classification. Computational Statis-tics and Data Analysis, 50(10), 2668–2684. https://doi.org/10.1016/j.csda.2005.04.012
Claeskens, G. (2016). Statistical Model Choice. Annual Review of Statistics and Its Application, 3(1), 233–256. https://doi.org/10.1146/annurev-statistics-041715-033413
Corduas, M., & Piccolo, D. (2008). Time series clustering and classification by the autoregressive metric. Computational Statistics and Data Analysis, 52(4), 1860–1872. https://doi.org/10.1016/j.csda.2007.06.001
Cryer, J. D., & Chan, K. (2008). Time Series Analysis with Application in R (2nd ed.). Springer.
Biabiany, E., Bernard, D.C., Page, V., Paugam-Moisy, H. (2020). Design of an expert distance metric for climate cluster-ing: The case of rainfall in the Lesser Antilles. Computers & Geosciences, 145. 104612. https://doi.org/10.1016/j.cageo.2020.104612
Ergüner Özkoç, E. (2021). Clustering of Time-Series Data. In Data Mining - Methods, Applications and Systems. IntechOpen. https://doi.org/10.5772/intechopen.84490
Eszergár-Kiss, D., & Caesar, B. (2017). Definition of user groups applying Ward’s method. Transportation Research Pro-cedia, 22, 25–34. https://doi.org/10.1016/j.trpro.2017.03.004
Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley.
Gan, G., Ma, C., & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. In Data Clustering: Theory, Al-gorithms, and Applications. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898718348
Gullo, F., Ponti, G., Tagarelli, A., Tradigo, G., & Veltri, P. (2012). A time series approach for clustering mass spectrome-try data. Journal of Computational Science, 3(5), 344–355. https://doi.org/10.1016/j.jocs.2011.06.008
Hendrawati, T., Wigena, A. H., Sumertajaya, I. M., & Sartono, B. (2020). A new approach to clustering time series data using the arima model uncertainty. Communications in Mathematical Biology and Neuroscience, 2020, 1–14. https://doi.org/10.28919/cmbn/4778
Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice Hall.
Javed, A., Lee, B. S., & Rizzo, D. M. (2020). A benchmark study on time series clustering. In arXiv, 1, p. 100001. https://doi.org/10.1016/j.mlwa.2020.100001
Kalpakis, K., Gada, D., & Puttagunta, V. (2001). Distance measures for effective clustering of ARIMA time-series. Pro-ceedings - IEEE International Conference on Data Mining, ICDM, 273–280. https://doi.org/10.1109/icdm.2001.989529
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data (L. Kaufman & P. J. Rousseeuw (eds.)). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470316801
Keogh, E., & Kasetty, S. (2003). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demon-stration. Data Mining and Knowledge Discovery, 7(4), 349–371. https://doi.org/10.1023/A:1024988512476
Kumar, M., & Patel, N. R. (2008). Clustering Data with Measurement Errors. In Statistical Methods in e-Commerce Re-search, 243–267. John Wiley and Sons Inc. https://doi.org/10.1002/9780470315262.ch11
Liao, T. W. (2005). Clustering of time series data - A survey. Pattern Recognition, 38(11), 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025
Maharaj, E. A. (2000). Clusters of time series. Journal of Classification, 17(2), 297–314. https://doi.org/10.1007/s003570000023
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Murtagh, F., & Legendre, P. (2014). Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Imple-ment Ward’s Criterion? Journal of Classification, 31(3), 274–295. https://doi.org/10.1007/s00357-014-9161-z
Piccolo, D. (1990). a Distance Measure for Classifying Arima Models. Journal of Time Series Analysis, 11(2), 153–164. https://doi.org/10.1111/j.1467-9892.1990.tb00048.x
Piccolo, D. (2010). The Autoregressive metric for comparing time series models. Statistica, 70(4), 459–480. https://doi.org/10.6092/issn.1973-2201/3598
Rani, S., & Sikka, G. (2012). Recent Techniques of Clustering of Time Series data: A Survey. International Journal of Computer Applications, 52(15), 1–9. https://doi.org/10.5120/8282-1278
Rahkmawati, Y., Sumertajaya, I.M,, Nur Aidi, M. (2019). Evaluation of Accuracy in Identification of ARIMA Models Based on Model Selection Criteria for Inflation Forecasting with the TSClust Approach. Int J Sci Res Publ. 9(9):p9355. doi:10.29322/ijsrp.9.09.2019.p9355.
Triacca, U. (2016). Measuring the distance between sets of ARMA models. Econometrics, 4(3). https://doi.org/10.3390/econometrics4030032
Wei, WW. (2006). Time Series Analysis: univariate and multivariate methods. second. Boston: Pearson Addison Wesley.
Anderson, D. R., & Burnham, K. P. (2002). Avoiding Pitfalls When Using Information-Theoretic Methods. The Journal of Wildlife Management, 66(3), 912. https://doi.org/10.2307/3803155
Brewer, M. J., Butler, A., & Cooksley, S. L. (2016). The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7(6), 679–692. https://doi.org/10.1111/2041-210X.12541
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Socio-logical Methods and Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644
Caiado, J., Crato, N., & Peña, D. (2006). A periodogram-based metric for time series classification. Computational Statis-tics and Data Analysis, 50(10), 2668–2684. https://doi.org/10.1016/j.csda.2005.04.012
Claeskens, G. (2016). Statistical Model Choice. Annual Review of Statistics and Its Application, 3(1), 233–256. https://doi.org/10.1146/annurev-statistics-041715-033413
Corduas, M., & Piccolo, D. (2008). Time series clustering and classification by the autoregressive metric. Computational Statistics and Data Analysis, 52(4), 1860–1872. https://doi.org/10.1016/j.csda.2007.06.001
Cryer, J. D., & Chan, K. (2008). Time Series Analysis with Application in R (2nd ed.). Springer.
Biabiany, E., Bernard, D.C., Page, V., Paugam-Moisy, H. (2020). Design of an expert distance metric for climate cluster-ing: The case of rainfall in the Lesser Antilles. Computers & Geosciences, 145. 104612. https://doi.org/10.1016/j.cageo.2020.104612
Ergüner Özkoç, E. (2021). Clustering of Time-Series Data. In Data Mining - Methods, Applications and Systems. IntechOpen. https://doi.org/10.5772/intechopen.84490
Eszergár-Kiss, D., & Caesar, B. (2017). Definition of user groups applying Ward’s method. Transportation Research Pro-cedia, 22, 25–34. https://doi.org/10.1016/j.trpro.2017.03.004
Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley.
Gan, G., Ma, C., & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. In Data Clustering: Theory, Al-gorithms, and Applications. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9780898718348
Gullo, F., Ponti, G., Tagarelli, A., Tradigo, G., & Veltri, P. (2012). A time series approach for clustering mass spectrome-try data. Journal of Computational Science, 3(5), 344–355. https://doi.org/10.1016/j.jocs.2011.06.008
Hendrawati, T., Wigena, A. H., Sumertajaya, I. M., & Sartono, B. (2020). A new approach to clustering time series data using the arima model uncertainty. Communications in Mathematical Biology and Neuroscience, 2020, 1–14. https://doi.org/10.28919/cmbn/4778
Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice Hall.
Javed, A., Lee, B. S., & Rizzo, D. M. (2020). A benchmark study on time series clustering. In arXiv, 1, p. 100001. https://doi.org/10.1016/j.mlwa.2020.100001
Kalpakis, K., Gada, D., & Puttagunta, V. (2001). Distance measures for effective clustering of ARIMA time-series. Pro-ceedings - IEEE International Conference on Data Mining, ICDM, 273–280. https://doi.org/10.1109/icdm.2001.989529
Kaufman, L., & Rousseeuw, P. J. (1990). Finding Groups in Data (L. Kaufman & P. J. Rousseeuw (eds.)). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470316801
Keogh, E., & Kasetty, S. (2003). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demon-stration. Data Mining and Knowledge Discovery, 7(4), 349–371. https://doi.org/10.1023/A:1024988512476
Kumar, M., & Patel, N. R. (2008). Clustering Data with Measurement Errors. In Statistical Methods in e-Commerce Re-search, 243–267. John Wiley and Sons Inc. https://doi.org/10.1002/9780470315262.ch11
Liao, T. W. (2005). Clustering of time series data - A survey. Pattern Recognition, 38(11), 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025
Maharaj, E. A. (2000). Clusters of time series. Journal of Classification, 17(2), 297–314. https://doi.org/10.1007/s003570000023
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
Murtagh, F., & Legendre, P. (2014). Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Imple-ment Ward’s Criterion? Journal of Classification, 31(3), 274–295. https://doi.org/10.1007/s00357-014-9161-z
Piccolo, D. (1990). a Distance Measure for Classifying Arima Models. Journal of Time Series Analysis, 11(2), 153–164. https://doi.org/10.1111/j.1467-9892.1990.tb00048.x
Piccolo, D. (2010). The Autoregressive metric for comparing time series models. Statistica, 70(4), 459–480. https://doi.org/10.6092/issn.1973-2201/3598
Rani, S., & Sikka, G. (2012). Recent Techniques of Clustering of Time Series data: A Survey. International Journal of Computer Applications, 52(15), 1–9. https://doi.org/10.5120/8282-1278
Rahkmawati, Y., Sumertajaya, I.M,, Nur Aidi, M. (2019). Evaluation of Accuracy in Identification of ARIMA Models Based on Model Selection Criteria for Inflation Forecasting with the TSClust Approach. Int J Sci Res Publ. 9(9):p9355. doi:10.29322/ijsrp.9.09.2019.p9355.
Triacca, U. (2016). Measuring the distance between sets of ARMA models. Econometrics, 4(3). https://doi.org/10.3390/econometrics4030032
Wei, WW. (2006). Time Series Analysis: univariate and multivariate methods. second. Boston: Pearson Addison Wesley.