Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation
Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second‐order structure of a high dimensional time series. In a ge...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 77; H. 2; S. 475 - 507 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Oxford
Royal Statistical Society
01.03.2015
Blackwell Publishing Ltd John Wiley & Sons Ltd Oxford University Press |
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| ISSN: | 1369-7412, 1467-9868 |
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| Abstract | Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second‐order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross‐periodograms of the components of the input time series. However, the standard ‘maximum’ and ‘average’ methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This ‘sparsifying’ step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work. |
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| AbstractList | Time series segmentation, which is also known as multiple‐ ;change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross‐ ;periodograms of the components of the input time series. However, the standard 'maximum' and 'average' methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This 'sparsifying' step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work. Reprinted by permission of Blackwell Publishers Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of the components of the input time series. However, the standard 'maximum' and 'average' methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This 'sparsifying' step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work. Summary Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second‐order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross‐periodograms of the components of the input time series. However, the standard ‘maximum’ and ‘average’ methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This ‘sparsifying’ step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work. Summary Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of the components of the input time series. However, the standard 'maximum' and 'average' methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This 'sparsifying' step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work. |
| Author | Cho, Haeran Fryzlewicz, Piotr |
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| Cites_doi | 10.1023/A:1016130108440 10.1080/01621459.1997.10474026 10.1137/1132110 10.1093/biomet/asq007 10.5705/ss.2009.280 10.1198/016214501753168244 10.1111/1467-9892.00172 10.1162/003465304323023886 10.1007/978-1-4612-1718-3 10.1111/rssb.12015 10.1111/j.1467-9868.2005.00532.x 10.1111/j.1467-9892.2012.00796.x 10.1214/09-AOS707 10.1111/j.1467-9868.2006.00558.x 10.1111/j.1467-9892.2008.00585.x 10.1007/BF02523391 10.1111/1467-9868.00231 10.1198/016214504000001448 10.1007/s11222-010-9200-5 10.1007/s10986-006-0028-9 10.1002/9780470317020 10.1111/j.1467-9892.2010.00685.x 10.1146/annurev-economics-061109-080451 10.1198/016214505000000745 10.1214/14-AOS1245 |
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| References | Vidakovic, B. (1999) Statistical Modeling by Wavelets. New York: Wiley. Sanderson, J., Fryzlewicz, P. and Jones, M. (2010) Estimating linear dependence between non-stationary time series using the locally stationary wavelet model. Biometrika, 97, 435-446. Cho, H. and Fryzlewicz, P. (2011) Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets. Statist. Comput., 21, 671-681. Mikosch, T. and Staˇricaˇ, C. (2004) Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Rev. Econ. Statist., 86, 378-390. Fryzlewicz, P., Van Bellegem, S. and von Sachs, R. (2003) Forecasting non-stationary time series by wavelet process modelling. Ann. Inst. Statist. Math., 55, 737-764. Chen, J. and Gupta, A. K. (1997) Testing and locating variance change-points with application to stock prices. J. Am. Statist. Ass., 92, 739-747. Korostelev, A. (1987) On minimax estimation of a discontinuous signal. Theor. Probab. Appl., 32, 727-730. Dwivedi, Y. and Subba Rao, S. (2011) A test for second-order stationarity of a time series based on the discrete Fourier transform. J. Time Ser. Anal., 32, 68-91. Ombao, H., von Sachs, R. and Guo, W. (2005) SLEX analysis of multivariate nonstationary time series. J. Am. Statist. Ass., 100, 519-531. Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B, 68, 49-67. Inclán, C. and Tiao, G. C. (1994) Use of cumulative sums of squares for retrospective detection of changes of variance. J. Am. Statist. Ass., 89, 913-923. Johnson, N. and Kotz, S. (1970) Distributions in Statistics: Continuous Univariate Distributions, vol. 1. Boston: Houghton Mifflin. Ombao, H. C., Raz, J. A., von Sachs, R. and Guo, W. (2002) The SLEX model of a non-stationary random process. Ann. Inst. Statist. Math., 54, 171-200. Aue, A., Hörmann, S., Horváth, L. and Reimherr, M. (2009) Break detection in the covariance structure of multivariate time series models. Ann. Statist., 37, 4046-4087. Ombao, H. C., Raz, J. A., von Sachs, R. and Malow, B. A. (2001) Automatic statistical analysis of bivariate nonstationary time series. J. Am. Statist. Ass., 96, 543-560. Nason, G. P. and Silverman, B. W. (1995) The Stationary Wavelet Transform and Some Statistical Applications, .281-300. New York: Springer. Davis, R. A., Lee, T. C. M. and Rodriguez-Yam, G. A. (2008) Break detection for a class of nonlinear time series models. J. Time Ser. Anal., 29, 834-867. Davis, R. A., Lee, T. C. M. and Rodriguez-Yam, G. A. (2006) Structural break estimation for non-stationary time series. J. Am. Statist. Ass., 101, 223-239. Fan, J., Lv, J. and Qi, L. (2011) Sparse high-dimensional models in economics. Ann. Rev. Econ., 3, 291-317. Fryzlewicz, P. (2005) Modelling and forecasting financial log-returns as locally stationary wavelet processes. J. App. Statist., 32, 503-528. Fryzlewicz, P. (2014) Wild Binary Segmentation for multiple change-point detection. Ann. Statist., to be published. Nason, G. P., von Sachs, R. and Kroisandt, G. (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J. R. Statist. Soc. B, 62, 271-292. Vert, J. and Bleakley, K. (2010) Fast detection of multiple change-points shared by many signals using group LARS. Adv. Neur. Inform. Process. Syst., 23, 2343-2351. Lavielle, M. and Teyssière, G. (2006) Detection of multiple change-points in multivariate time series. Lith. Math. J., 46, 287-306. Bosq, D. (1998) Nonparametric Statistics for Stochastic Processes: Estimation and Prediction. New York: Springer. Groen, J., Kapetanios, G. and Price, S. (2013) Multivariate methods for monitoring structural change. J. Multiv. Time Ser., 28, 250-274. Horváth, L. and Hušková, M. (2012) Change-point detection in panel data. J. Time Ser. Anal., 33, 631-648. Fryzlewicz, P. and Nason, G. (2006) Haar-Fisz estimation of evolutionary wavelet spectra. J. R. Statist. Soc. B, 68, 611-634. Lavielle, M. and Moulines, E. (2000) Least-squares estimation of an unknown number of shifts in a time series. J. Time Ser. Anal., 21, 33-59. Nason, G. P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. Cho, H. and Fryzlewicz, P. (2012) Multiscale and multilevel technique for consistent segmentation of nonstationary time series. Statist. Sin., 22, 207-229. 2004; 86 2010; 97 1987; 32 2013; 28 2000; 21 2002; 54 1998 1994; 89 2011; 32 1995 1970 1992 2011; 3 2012; 33 2003; 55 1999 2010; 23 1997; 92 2006; 68 2006; 46 2005; 100 2008; 29 2013; 75 2000; 62 2011; 21 2005; 32 2014 2013 2012; 22 2006; 101 2009; 37 2001; 96 Horváth (2023021709291111800_rssb12079-cit-0015) 2012; 33 Ombao (2023021709291111800_rssb12079-cit-0028) 2005; 100 Lavielle (2023021709291111800_rssb12079-cit-0020) 2000; 21 Fryzlewicz (2023021709291111800_rssb12079-cit-0011) 2014 Nason (2023021709291111800_rssb12079-cit-0025) 1995 Venkatraman (2023021709291111800_rssb12079-cit-0030) 1992 Fryzlewicz (2023021709291111800_rssb12079-cit-0013) 2003; 55 Groen (2023021709291111800_rssb12079-cit-0014) 2013; 28 Ombao (2023021709291111800_rssb12079-cit-0026) 2002; 54 Aue (2023021709291111800_rssb12079-cit-0001) 2009; 37 Davis (2023021709291111800_rssb12079-cit-0006) 2006; 101 Vidakovic (2023021709291111800_rssb12079-cit-0032) 1999 Lavielle (2023021709291111800_rssb12079-cit-0021) 2006; 46 Sanderson (2023021709291111800_rssb12079-cit-0029) 2010; 97 Fryzlewicz (2023021709291111800_rssb12079-cit-0012) 2006; 68 Nason (2023021709291111800_rssb12079-cit-0023) 2013; 75 Fryzlewicz (2023021709291111800_rssb12079-cit-0010) 2005; 32 Mikosch (2023021709291111800_rssb12079-cit-0022) 2004; 86 Nason (2023021709291111800_rssb12079-cit-0024) 2000; 62 Ombao (2023021709291111800_rssb12079-cit-0027) 2001; 96 Cho (2023021709291111800_rssb12079-cit-0004) 2011; 21 Korostelev (2023021709291111800_rssb12079-cit-0019) 1987; 32 Fan (2023021709291111800_rssb12079-cit-0009) 2011; 3 Inclán (2023021709291111800_rssb12079-cit-0016) 1994; 89 Chen (2023021709291111800_rssb12079-cit-0003) 1997; 92 Bosq (2023021709291111800_rssb12079-cit-0002) 1998 Johnson (2023021709291111800_rssb12079-cit-0018) 1970 Cho (2023021709291111800_rssb12079-cit-0005) 2012; 22 Yuan (2023021709291111800_rssb12079-cit-0033) 2006; 68 Jentsch (2023021709291111800_rssb12079-cit-0017) 2013 Dwivedi (2023021709291111800_rssb12079-cit-0008) 2011; 32 Vert (2023021709291111800_rssb12079-cit-0031) 2010; 23 Davis (2023021709291111800_rssb12079-cit-0007) 2008; 29 |
| References_xml | – reference: Ombao, H., von Sachs, R. and Guo, W. (2005) SLEX analysis of multivariate nonstationary time series. J. Am. Statist. Ass., 100, 519-531. – reference: Cho, H. and Fryzlewicz, P. (2011) Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets. Statist. Comput., 21, 671-681. – reference: Davis, R. A., Lee, T. C. M. and Rodriguez-Yam, G. A. (2006) Structural break estimation for non-stationary time series. J. Am. Statist. Ass., 101, 223-239. – reference: Inclán, C. and Tiao, G. C. (1994) Use of cumulative sums of squares for retrospective detection of changes of variance. J. Am. Statist. Ass., 89, 913-923. – reference: Fan, J., Lv, J. and Qi, L. (2011) Sparse high-dimensional models in economics. Ann. Rev. Econ., 3, 291-317. – reference: Mikosch, T. and Staˇricaˇ, C. (2004) Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Rev. Econ. Statist., 86, 378-390. – reference: Nason, G. P. (2013) A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J. R. Statist. Soc. B, 75, 879-904. – reference: Fryzlewicz, P. (2014) Wild Binary Segmentation for multiple change-point detection. Ann. Statist., to be published. – reference: Lavielle, M. and Teyssière, G. (2006) Detection of multiple change-points in multivariate time series. Lith. Math. J., 46, 287-306. – reference: Bosq, D. (1998) Nonparametric Statistics for Stochastic Processes: Estimation and Prediction. New York: Springer. – reference: Davis, R. A., Lee, T. C. M. and Rodriguez-Yam, G. A. (2008) Break detection for a class of nonlinear time series models. J. Time Ser. Anal., 29, 834-867. – reference: Groen, J., Kapetanios, G. and Price, S. (2013) Multivariate methods for monitoring structural change. J. Multiv. Time Ser., 28, 250-274. – reference: Sanderson, J., Fryzlewicz, P. and Jones, M. (2010) Estimating linear dependence between non-stationary time series using the locally stationary wavelet model. Biometrika, 97, 435-446. – reference: Horváth, L. and Hušková, M. (2012) Change-point detection in panel data. J. Time Ser. Anal., 33, 631-648. – reference: Fryzlewicz, P. and Nason, G. (2006) Haar-Fisz estimation of evolutionary wavelet spectra. J. R. Statist. Soc. B, 68, 611-634. – reference: Dwivedi, Y. and Subba Rao, S. (2011) A test for second-order stationarity of a time series based on the discrete Fourier transform. J. Time Ser. Anal., 32, 68-91. – reference: Cho, H. and Fryzlewicz, P. (2012) Multiscale and multilevel technique for consistent segmentation of nonstationary time series. Statist. Sin., 22, 207-229. – reference: Johnson, N. and Kotz, S. (1970) Distributions in Statistics: Continuous Univariate Distributions, vol. 1. Boston: Houghton Mifflin. – reference: Lavielle, M. and Moulines, E. (2000) Least-squares estimation of an unknown number of shifts in a time series. J. Time Ser. Anal., 21, 33-59. – reference: Chen, J. and Gupta, A. K. (1997) Testing and locating variance change-points with application to stock prices. J. Am. Statist. Ass., 92, 739-747. – reference: Fryzlewicz, P. (2005) Modelling and forecasting financial log-returns as locally stationary wavelet processes. J. App. Statist., 32, 503-528. – reference: Ombao, H. C., Raz, J. A., von Sachs, R. and Malow, B. A. (2001) Automatic statistical analysis of bivariate nonstationary time series. J. Am. Statist. Ass., 96, 543-560. – reference: Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B, 68, 49-67. – reference: Fryzlewicz, P., Van Bellegem, S. and von Sachs, R. (2003) Forecasting non-stationary time series by wavelet process modelling. Ann. Inst. Statist. Math., 55, 737-764. – reference: Korostelev, A. (1987) On minimax estimation of a discontinuous signal. Theor. Probab. Appl., 32, 727-730. – reference: Ombao, H. C., Raz, J. A., von Sachs, R. and Guo, W. (2002) The SLEX model of a non-stationary random process. Ann. Inst. Statist. Math., 54, 171-200. – reference: Nason, G. P., von Sachs, R. and Kroisandt, G. (2000) Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J. R. Statist. Soc. B, 62, 271-292. – reference: Vert, J. and Bleakley, K. (2010) Fast detection of multiple change-points shared by many signals using group LARS. Adv. Neur. Inform. Process. Syst., 23, 2343-2351. – reference: Vidakovic, B. (1999) Statistical Modeling by Wavelets. New York: Wiley. – reference: Aue, A., Hörmann, S., Horváth, L. and Reimherr, M. (2009) Break detection in the covariance structure of multivariate time series models. Ann. Statist., 37, 4046-4087. – reference: Nason, G. P. and Silverman, B. W. (1995) The Stationary Wavelet Transform and Some Statistical Applications, .281-300. New York: Springer. – volume: 23 start-page: 2343 year: 2010 end-page: 2351 article-title: Fast detection of multiple change‐points shared by many signals using group LARS publication-title: Adv. Neur. Inform. Process. Syst. – volume: 22 start-page: 207 year: 2012 end-page: 229 article-title: Multiscale and multilevel technique for consistent segmentation of nonstationary time series publication-title: Statist. Sin. – volume: 101 start-page: 223 year: 2006 end-page: 239 article-title: Structural break estimation for non‐stationary time series publication-title: J. Am. Statist. Ass. – volume: 89 start-page: 913 year: 1994 end-page: 923 article-title: Use of cumulative sums of squares for retrospective detection of changes of variance publication-title: J. Am. Statist. Ass. – volume: 62 start-page: 271 year: 2000 end-page: 292 article-title: Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum publication-title: J. R. Statist. Soc. B – volume: 32 start-page: 727 year: 1987 end-page: 730 article-title: On minimax estimation of a discontinuous signal publication-title: Theor. Probab. Appl. – volume: 68 start-page: 611 year: 2006 end-page: 634 article-title: Haar–Fisz estimation of evolutionary wavelet spectra publication-title: J. R. Statist. Soc. B – volume: 97 start-page: 435 year: 2010 end-page: 446 article-title: Estimating linear dependence between non‐stationary time series using the locally stationary wavelet model publication-title: Biometrika – volume: 28 start-page: 250 year: 2013 end-page: 274 article-title: Multivariate methods for monitoring structural change publication-title: J. Multiv. Time Ser. – volume: 37 start-page: 4046–4087 year: 2009 article-title: Break detection in the covariance structure of multivariate time series models publication-title: Ann. Statist. – volume: 96 start-page: 543 year: 2001 end-page: 560 article-title: Automatic statistical analysis of bivariate nonstationary time series publication-title: J. Am. Statist. Ass. – volume: 55 start-page: 737 year: 2003 end-page: 764 article-title: Forecasting non‐stationary time series by wavelet process modelling publication-title: Ann. Inst. Statist. Math. – year: 1992 – volume: 86 start-page: 378 year: 2004 end-page: 390 article-title: Nonstationarities in financial time series, the long‐range dependence, and the IGARCH effects publication-title: Rev. Econ. Statist. – volume: 54 start-page: 171 year: 2002 end-page: 200 article-title: The SLEX model of a non‐stationary random process publication-title: Ann. Inst. Statist. Math. – year: 1998 – volume: 29 start-page: 834 year: 2008 end-page: 867 article-title: Break detection for a class of nonlinear time series models publication-title: J. Time Ser. Anal. – volume: 75 start-page: 879 year: 2013 end-page: 904 article-title: A test for second‐order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series publication-title: J. R. Statist. Soc. B – volume: 100 start-page: 519 year: 2005 end-page: 531 article-title: SLEX analysis of multivariate nonstationary time series. publication-title: Am. Statist. Ass. – volume: 21 start-page: 33 year: 2000 end-page: 59 article-title: Least‐squares estimation of an unknown number of shifts in a time series publication-title: J. Time Ser. Anal. – volume: 92 start-page: 739 year: 1997 end-page: 747 article-title: Testing and locating variance change‐points with application to stock prices publication-title: J. Am. Statist. Ass. – volume: 21 start-page: 671 year: 2011 end-page: 681 article-title: Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets publication-title: Statist. Comput. – start-page: 281 year: 1995 end-page: 300 – volume: 33 start-page: 631 year: 2012 end-page: 648 article-title: Change‐point detection in panel data publication-title: J. Time Ser. Anal. – volume: 68 start-page: 49 year: 2006 end-page: 67 article-title: Model selection and estimation in regression with grouped variables publication-title: J. R. Statist. Soc. B – volume: 32 start-page: 68 year: 2011 end-page: 91 article-title: A test for second‐order stationarity of a time series based on the discrete Fourier transform publication-title: J. Time Ser. Anal. – volume: 32 start-page: 503 year: 2005 end-page: 528 article-title: Modelling and forecasting financial log‐returns as locally stationary wavelet processes publication-title: J. App. Statist. – volume: 46 start-page: 287 year: 2006 end-page: 306 article-title: Detection of multiple change‐points in multivariate time series publication-title: Lith. Math. J. – year: 2014 article-title: Wild Binary Segmentation for multiple change‐point detection publication-title: Ann. Statist. – volume: 3 start-page: 291 year: 2011 end-page: 317 article-title: Sparse high‐dimensional models in economics publication-title: Ann. Rev. Econ. – year: 1970 – year: 2013 – year: 1999 – volume: 54 start-page: 171 year: 2002 ident: 2023021709291111800_rssb12079-cit-0026 article-title: The SLEX model of a non-stationary random process publication-title: Ann. Inst. Statist. Math. doi: 10.1023/A:1016130108440 – volume: 92 start-page: 739 year: 1997 ident: 2023021709291111800_rssb12079-cit-0003 article-title: Testing and locating variance change-points with application to stock prices publication-title: J. Am. Statist. Ass. doi: 10.1080/01621459.1997.10474026 – start-page: 281 volume-title: The Stationary Wavelet Transform and Some Statistical Applications year: 1995 ident: 2023021709291111800_rssb12079-cit-0025 – volume: 89 start-page: 913 year: 1994 ident: 2023021709291111800_rssb12079-cit-0016 article-title: Use of cumulative sums of squares for retrospective detection of changes of variance publication-title: J. Am. Statist. Ass. – volume: 32 start-page: 727 year: 1987 ident: 2023021709291111800_rssb12079-cit-0019 article-title: On minimax estimation of a discontinuous signal publication-title: Theor. Probab. Appl. doi: 10.1137/1132110 – volume: 97 start-page: 435 year: 2010 ident: 2023021709291111800_rssb12079-cit-0029 article-title: Estimating linear dependence between non-stationary time series using the locally stationary wavelet model publication-title: Biometrika doi: 10.1093/biomet/asq007 – volume: 23 start-page: 2343 year: 2010 ident: 2023021709291111800_rssb12079-cit-0031 article-title: Fast detection of multiple change-points shared by many signals using group LARS publication-title: Adv. Neur. Inform. Process. Syst. – volume: 22 start-page: 207 year: 2012 ident: 2023021709291111800_rssb12079-cit-0005 article-title: Multiscale and multilevel technique for consistent segmentation of nonstationary time series publication-title: Statist. Sin. doi: 10.5705/ss.2009.280 – volume: 96 start-page: 543 year: 2001 ident: 2023021709291111800_rssb12079-cit-0027 article-title: Automatic statistical analysis of bivariate nonstationary time series publication-title: J. Am. Statist. Ass. doi: 10.1198/016214501753168244 – volume-title: Distributions in Statistics: Continuous Univariate Distributions year: 1970 ident: 2023021709291111800_rssb12079-cit-0018 – volume: 21 start-page: 33 year: 2000 ident: 2023021709291111800_rssb12079-cit-0020 article-title: Least-squares estimation of an unknown number of shifts in a time series publication-title: J. Time Ser. Anal. doi: 10.1111/1467-9892.00172 – volume: 86 start-page: 378 year: 2004 ident: 2023021709291111800_rssb12079-cit-0022 article-title: Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects publication-title: Rev. Econ. Statist. doi: 10.1162/003465304323023886 – volume-title: Nonparametric Statistics for Stochastic Processes: Estimation and Prediction year: 1998 ident: 2023021709291111800_rssb12079-cit-0002 doi: 10.1007/978-1-4612-1718-3 – volume: 75 start-page: 879 year: 2013 ident: 2023021709291111800_rssb12079-cit-0023 article-title: A test for second-order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series publication-title: J. R. Statist. Soc. B doi: 10.1111/rssb.12015 – volume: 68 start-page: 49 year: 2006 ident: 2023021709291111800_rssb12079-cit-0033 article-title: Model selection and estimation in regression with grouped variables publication-title: J. R. Statist. Soc. B doi: 10.1111/j.1467-9868.2005.00532.x – volume: 33 start-page: 631 year: 2012 ident: 2023021709291111800_rssb12079-cit-0015 article-title: Change-point detection in panel data publication-title: J. Time Ser. Anal. doi: 10.1111/j.1467-9892.2012.00796.x – volume: 37 start-page: 4046–4087 year: 2009 ident: 2023021709291111800_rssb12079-cit-0001 article-title: Break detection in the covariance structure of multivariate time series models publication-title: Ann. Statist. doi: 10.1214/09-AOS707 – volume: 28 start-page: 250 year: 2013 ident: 2023021709291111800_rssb12079-cit-0014 article-title: Multivariate methods for monitoring structural change publication-title: J. Multiv. Time Ser. – volume: 68 start-page: 611 year: 2006 ident: 2023021709291111800_rssb12079-cit-0012 article-title: Haar–Fisz estimation of evolutionary wavelet spectra publication-title: J. R. Statist. Soc. B doi: 10.1111/j.1467-9868.2006.00558.x – volume: 29 start-page: 834 year: 2008 ident: 2023021709291111800_rssb12079-cit-0007 article-title: Break detection for a class of nonlinear time series models publication-title: J. Time Ser. Anal. doi: 10.1111/j.1467-9892.2008.00585.x – volume: 55 start-page: 737 year: 2003 ident: 2023021709291111800_rssb12079-cit-0013 article-title: Forecasting non-stationary time series by wavelet process modelling publication-title: Ann. Inst. Statist. Math. doi: 10.1007/BF02523391 – volume-title: A test for second order stationarity of a multivariate time series year: 2013 ident: 2023021709291111800_rssb12079-cit-0017 – volume: 62 start-page: 271 year: 2000 ident: 2023021709291111800_rssb12079-cit-0024 article-title: Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum publication-title: J. R. Statist. Soc. B doi: 10.1111/1467-9868.00231 – volume: 32 start-page: 503 year: 2005 ident: 2023021709291111800_rssb12079-cit-0010 article-title: Modelling and forecasting financial log-returns as locally stationary wavelet processes publication-title: J. App. Statist. – volume: 100 start-page: 519 year: 2005 ident: 2023021709291111800_rssb12079-cit-0028 article-title: SLEX analysis of multivariate nonstationary time series. J publication-title: Am. Statist. Ass. doi: 10.1198/016214504000001448 – volume: 21 start-page: 671 year: 2011 ident: 2023021709291111800_rssb12079-cit-0004 article-title: Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets publication-title: Statist. Comput. doi: 10.1007/s11222-010-9200-5 – volume: 46 start-page: 287 year: 2006 ident: 2023021709291111800_rssb12079-cit-0021 article-title: Detection of multiple change-points in multivariate time series publication-title: Lith. Math. J. doi: 10.1007/s10986-006-0028-9 – volume-title: Statistical Modeling by Wavelets year: 1999 ident: 2023021709291111800_rssb12079-cit-0032 doi: 10.1002/9780470317020 – volume: 32 start-page: 68 year: 2011 ident: 2023021709291111800_rssb12079-cit-0008 article-title: A test for second-order stationarity of a time series based on the discrete Fourier transform publication-title: J. Time Ser. Anal. doi: 10.1111/j.1467-9892.2010.00685.x – volume: 3 start-page: 291 year: 2011 ident: 2023021709291111800_rssb12079-cit-0009 article-title: Sparse high-dimensional models in economics publication-title: Ann. Rev. Econ. doi: 10.1146/annurev-economics-061109-080451 – volume-title: Consistency results in multiple change-point problems year: 1992 ident: 2023021709291111800_rssb12079-cit-0030 – volume: 101 start-page: 223 year: 2006 ident: 2023021709291111800_rssb12079-cit-0006 article-title: Structural break estimation for non-stationary time series publication-title: J. Am. Statist. Ass. doi: 10.1198/016214505000000745 – year: 2014 ident: 2023021709291111800_rssb12079-cit-0011 article-title: Wild Binary Segmentation for multiple change-point detection publication-title: Ann. Statist. doi: 10.1214/14-AOS1245 |
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| Snippet | Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been designed... Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed... Summary Time series segmentation, which is also known as multiple‐change‐point detection, is a well‐established problem. However, few solutions have been... Summary Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been... Time series segmentation, which is also known as multiple‐ ;change-point detection, is a well-established problem. However, few solutions have been designed... |
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| Title | Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation |
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