Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes
Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kazor, Karen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© International Biometric Society 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of agricultural, biological, and environmental statistics - New York, NY : Springer, 1996, 20(2015), 2 vom: 29. Apr., Seite 192-217 |
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| Übergeordnetes Werk: |
volume:20 ; year:2015 ; number:2 ; day:29 ; month:04 ; pages:192-217 |
| Links: |
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| DOI / URN: |
10.1007/s13253-015-0203-8 |
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| 520 | |a Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. | ||
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| 700 | 1 | |a Hering, Amanda S. |4 aut | |
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10.1007/s13253-015-0203-8 doi (DE-627)SPR03102095X (SPR)s13253-015-0203-8-e DE-627 ger DE-627 rakwb eng Kazor, Karen verfasserin aut Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 Hering, Amanda S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 20(2015), 2 vom: 29. Apr., Seite 192-217 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:20 year:2015 number:2 day:29 month:04 pages:192-217 https://dx.doi.org/10.1007/s13253-015-0203-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 20 2015 2 29 04 192-217 |
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10.1007/s13253-015-0203-8 doi (DE-627)SPR03102095X (SPR)s13253-015-0203-8-e DE-627 ger DE-627 rakwb eng Kazor, Karen verfasserin aut Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 Hering, Amanda S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 20(2015), 2 vom: 29. Apr., Seite 192-217 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:20 year:2015 number:2 day:29 month:04 pages:192-217 https://dx.doi.org/10.1007/s13253-015-0203-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 20 2015 2 29 04 192-217 |
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10.1007/s13253-015-0203-8 doi (DE-627)SPR03102095X (SPR)s13253-015-0203-8-e DE-627 ger DE-627 rakwb eng Kazor, Karen verfasserin aut Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 Hering, Amanda S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 20(2015), 2 vom: 29. Apr., Seite 192-217 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:20 year:2015 number:2 day:29 month:04 pages:192-217 https://dx.doi.org/10.1007/s13253-015-0203-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 20 2015 2 29 04 192-217 |
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10.1007/s13253-015-0203-8 doi (DE-627)SPR03102095X (SPR)s13253-015-0203-8-e DE-627 ger DE-627 rakwb eng Kazor, Karen verfasserin aut Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 Hering, Amanda S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 20(2015), 2 vom: 29. Apr., Seite 192-217 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:20 year:2015 number:2 day:29 month:04 pages:192-217 https://dx.doi.org/10.1007/s13253-015-0203-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 20 2015 2 29 04 192-217 |
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10.1007/s13253-015-0203-8 doi (DE-627)SPR03102095X (SPR)s13253-015-0203-8-e DE-627 ger DE-627 rakwb eng Kazor, Karen verfasserin aut Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Biometric Society 2015 Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 Hering, Amanda S. aut Enthalten in Journal of agricultural, biological, and environmental statistics New York, NY : Springer, 1996 20(2015), 2 vom: 29. Apr., Seite 192-217 (DE-627)327130652 (DE-600)2043957-X 1537-2693 nnns volume:20 year:2015 number:2 day:29 month:04 pages:192-217 https://dx.doi.org/10.1007/s13253-015-0203-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2938 GBV_ILN_2947 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 AR 20 2015 2 29 04 192-217 |
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In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. 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Kazor, Karen |
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Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes Forecasting (dpeaa)DE-He213 Gaussian mixture models (dpeaa)DE-He213 -Means (dpeaa)DE-He213 Markov-switching models (dpeaa)DE-He213 Nonparametric mixture models (dpeaa)DE-He213 |
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assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes |
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Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes |
| abstract |
Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015 |
| abstractGer |
Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015 |
| abstract_unstemmed |
Abstract The desire to group observations generated from multivariate time series is common in many applications with the goal to distinguish not only between differences in the means of individual variables but also changes in their covariances and in the temporal dependence of observations. In this analysis, we compare ten model-based clustering methods in terms of their ability to identify such features under four scenarios in which data are simulated with varying levels of variable and temporal dependence. To consider these methods in a realistic environment, we focus our analysis on wind data, where observations are often strongly correlated in time, and the dependence of variables is known to vary across different regional weather patterns. In particular, we assess each method’s performance when applied to wind data simulated under a realistic two-regime Markov-switching vector autoregressive (VAR) model with a diurnally varying mean. A Gaussian mixture model and a basic Markov-switching model outperform the other methods considered in terms of misclassification rates and number of clusters identified. These two methods and an additional Markov-switching VAR model are then applied to one year of averaged hourly wind data from twenty meteorological stations, and we find that the methods can identify very different features in the data. Supplementary materials accompanying this paper appear on-line. © International Biometric Society 2015 |
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Assessing the Performance of Model-Based Clustering Methods in Multivariate Time Series with Application to Identifying Regional Wind Regimes |
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https://dx.doi.org/10.1007/s13253-015-0203-8 |
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Hering, Amanda S. |
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| score |
7.3994513 |