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Volumn 14, Issue 3, 2013, Pages 1360-1369

An online change-point-based model for traffic parameter prediction

Author keywords

Change point models; hidden Markov model (HMM); time series autoregressive integrated moving average (ARIMA); traffic prediction

Indexed keywords

ADAPTIVE FORECASTING; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; CHANGE-POINT MODELS; DRIVING CHARACTERISTICS; EXPECTATION-MAXIMIZATION ALGORITHMS; FORECASTING MODELING; NUMERICAL EXPERIMENTS; TRAFFIC PREDICTION;

EID: 84883773412     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2013.2260540     Document Type: Article
Times cited : (68)

References (67)
  • 1
    • 1842760558 scopus 로고    scopus 로고
    • Forecasting economic and financial time-series with non-linear models
    • Apr.-Jun. 2004
    • M. P. Clements, P. H. Franses, and N. R. Swanson, Forecasting economic and financial time-series with non-linear models, Int. J. Forecast., vol. 20, no. 2, pp. 169-183, Apr.-Jun. 2004.
    • Int. J. Forecast. , vol.20 , Issue.2 , pp. 169-183
    • Clements, M.P.1    Franses, P.H.2    Swanson, N.R.3
  • 2
    • 0036692982 scopus 로고    scopus 로고
    • Comparison of parametric and nonparametric models for traffic flow forecasting
    • Aug. 2002
    • B. L. Smith, B.M.Williams, and R. K. Oswald, Comparison of parametric and nonparametric models for traffic flow forecasting, Transp. Res. C, Emerg. Technol., vol. 10, no. 4, pp. 303-321, Aug. 2002.
    • Transp. Res. C, Emerg. Technol. , vol.10 , Issue.4 , pp. 303-321
    • Smith, B.L.1    Williams, B.M.2    Oswald, R.K.3
  • 3
    • 0037954189 scopus 로고    scopus 로고
    • A multivariate state space approach for urban traffic flow modeling and prediction
    • Apr. 2003
    • A. Stathopoulos and M. G. Karlaftis, A multivariate state space approach for urban traffic flow modeling and prediction, Transp. Res. C, Emerg. Technol., vol. 11, no. 2, pp. 121-135, Apr. 2003.
    • Transp. Res. C, Emerg. Technol. , vol.11 , Issue.2 , pp. 121-135
    • Stathopoulos, A.1    Karlaftis, M.G.2
  • 4
    • 0344944192 scopus 로고    scopus 로고
    • Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results
    • Nov. 2003
    • B. M. Williams and L. A. Hoel, Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results, J. Transp. Eng., vol. 129, no. 6, pp. 664-672, Nov. 2003.
    • J. Transp. Eng. , vol.129 , Issue.6 , pp. 664-672
    • Williams, B.M.1    Hoel, L.A.2
  • 5
    • 67650224457 scopus 로고    scopus 로고
    • Multivariate short-term traffic flow forecasting using time-series analysis
    • Jun. 2009
    • B. Ghosh, B. Basu, and M. O?Mahony, Multivariate short-term traffic flow forecasting using time-series analysis, IEEE Trans. Intell. Transp. Syst., vol. 10, no. 2, pp. 246-254, Jun. 2009.
    • IEEE Trans. Intell. Transp. Syst. , vol.10 , Issue.2 , pp. 246-254
    • Ghosh, B.1    Basu, B.2    Omahony, M.3
  • 7
    • 84883775174 scopus 로고    scopus 로고
    • Comparison of univariate and multivariate time series models in short-term freeway traffic flow forecasting
    • Papers DVD, Washington, DC, USA
    • M. T. Lee and B. Friedrich, Comparison of univariate and multivariate time series models in short-term freeway traffic flow forecasting, in Proc. TRB 89th Annu. Meeting Compend. Papers DVD, Washington, DC, USA, 2010, vol. 1, pp. 1-13.
    • (2010) Proc. TRB 89th Annu. Meeting Compend , vol.1 , pp. 1-13
    • Lee, M.T.1    Friedrich, B.2
  • 8
    • 84861914607 scopus 로고    scopus 로고
    • Real-time traffic flow forecasting using spectral analysis
    • Jun. 2012
    • T. T. Tchrakian, B. Basu, and M. O?Mahony, Real-time traffic flow forecasting using spectral analysis, IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 519-526, Jun. 2012.
    • IEEE Trans. Intell. Transp. Syst. , vol.13 , Issue.2 , pp. 519-526
    • Tchrakian, T.T.1    Basu, B.2    Omahony, M.3
  • 9
    • 0031092040 scopus 로고    scopus 로고
    • Tracking and predicting a network traffic process
    • Mar. 1997
    • J.Whittaker, S. Garside, and K. Lindveld, Tracking and predicting a network traffic process, Int. J. Forecast., vol. 13, no. 1, pp. 51-61, Mar. 1997.
    • Int. J. Forecast. , vol.13 , Issue.1 , pp. 51-61
    • Whittaker, J.1    Garside, S.2    Lindveld, K.3
  • 10
    • 11144262651 scopus 로고    scopus 로고
    • Real-time freeway traffic state estimation based on extended Kalman filter: A general approach
    • Feb. 2005
    • Y.Wang andM. Papageorgiou, Real-time freeway traffic state estimation based on extended Kalman filter: A general approach, Transp. Res. B, Methodol., vol. 39, no. 2, pp. 141-167, Feb. 2005.
    • Transp. Res. B, Methodol. , vol.39 , Issue.2 , pp. 141-167
  • 11
    • 14744291722 scopus 로고    scopus 로고
    • An on-line recursive short-term traffic prediction algorithm
    • Jan. 2004
    • F. Yang, Z. Yin, H. X. Liu, and B. Ran, An on-line recursive short-term traffic prediction algorithm, Transp. Res. Rec., J. Transp. Res. Board, vol. 1879, no. 1, pp. 1-8, Jan. 2004.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1879 , Issue.1 , pp. 1-8
    • Yang, F.1    Yin, Z.2    Liu, H.X.3    Ran, B.4
  • 12
    • 67949085060 scopus 로고    scopus 로고
    • A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting
    • Oct. 2009
    • S. Huang and W. A. Sadek, A novel forecasting approach inspired by human memory: The example of short-term traffic volume forecasting, Transp. Res. C, Emerg. Technol., vol. 17, no. 5, pp. 510-525, Oct. 2009.
    • Transp. Res. C, Emerg. Technol. , vol.17 , Issue.5 , pp. 510-525
    • Huang, S.1    Sadek, W.A.2
  • 13
    • 0021375695 scopus 로고    scopus 로고
    • Dynamic prediction of traffic volume through Kalman filtering theory
    • Feb. 1984
    • I. Okutani and Y. Stephanedes, Dynamic prediction of traffic volume through Kalman filtering theory, Transp. Res. B, Methodol., vol. 18, no. 1, pp. 1-11, Feb. 1984.
    • Transp. Res. B, Methodol. , vol.18 , Issue.1 , pp. 1-11
    • Okutani, I.1    Stephanedes, Y.2
  • 14
    • 23844513726 scopus 로고    scopus 로고
    • Optimized and metaoptimized neural networks for short-term traffic flow prediction: A genetic approach
    • Jun. 2005
    • E. I. Vlahogianni,M. G. Karlaftis, and J. C. Golias, Optimized and metaoptimized neural networks for short-term traffic flow prediction: A genetic approach, Transp. Res. C, Emerg. Technol., vol. 13, no. 3, pp. 211-234, Jun. 2005.
    • Transp. Res. C, Emerg. Technol. , vol.13 , Issue.3 , pp. 211-234
    • Vlahogianni, M.G.1    Karlaftis, E.I.2    Golias, J.C.3
  • 15
    • 84883800910 scopus 로고    scopus 로고
    • Freeway travel time prediction with dynamic neural networks
    • Papers DVD, Washington, DC, USA, Jan. 2010
    • L. Shen and M. Hadi, Freeway travel time prediction with dynamic neural networks, in Proc. TRB 89th Annu. Meeting Compend. Papers DVD, Washington, DC, USA, Jan. 2010, vol. 1, pp. 1-15.
    • Proc. TRB 89th Annu. Meeting Compend , vol.1 , pp. 1-15
    • Shen, L.1    Hadi, M.2
  • 16
    • 70350294305 scopus 로고    scopus 로고
    • Near-term travel speed prediction utilizing Hilbert-Huang transform
    • Nov. 2009
    • K. Hamad, M. T. Shourijeh, E. Lee, and A. Faghri, Near-term travel speed prediction utilizing Hilbert-Huang transform, Comput.-Aided Civil Infrastruct. Eng., vol. 24, no. 8, pp. 551-576, Nov. 2009.
    • Comput.-Aided Civil Infrastruct. Eng. , vol.24 , Issue.8 , pp. 551-576
    • Hamad, K.1    Shourijeh, M.T.2    Lee, E.3    Faghri, A.4
  • 17
    • 84883808595 scopus 로고    scopus 로고
    • Intelligent intersection traffic flow prediction based on fuzzy neutral network
    • Papers DVD, Washington, DC, USA, Jan. 2010
    • Y. Zhang and H. Ge, Intelligent intersection traffic flow prediction based on fuzzy neutral network, in Proc. TRB 89th Annu. Meeting Compend. Papers DVD, Washington, DC, USA, Jan. 2010, vol. 1, pp. 1-19.
    • Proc. TRB 89th Annu. Meeting Compend , vol.1 , pp. 1-19
    • Zhang, Y.1    Ge, H.2
  • 18
    • 79951775181 scopus 로고    scopus 로고
    • Statistical methods versus neural networks in transportation research: Differences, similarities, and some insights
    • Jun. 2011
    • E. I. Vlahogianni and M. G. Karlaftis, Statistical methods versus neural networks in transportation research: Differences, similarities, and some insights, Transp. Res. C, Emerg. Technol., vol. 19, no. 3, pp. 387-399, Jun. 2011.
    • Transp. Res. C, Emerg. Technol. , vol.19 , Issue.3 , pp. 387-399
    • Vlahogianni, E.I.1    Karlaftis, M.G.2
  • 19
    • 31044437283 scopus 로고    scopus 로고
    • Short-term freeway traffic flow prediction: Bayesian combined neural network approach
    • Feb. 2006
    • W. Zheng, D. H. Lee, and Q. Shi, Short-term freeway traffic flow prediction: Bayesian combined neural network approach, J. Transp. Eng., vol. 132, no. 2, pp. 114-121, Feb. 2006.
    • J. Transp. Eng. , vol.132 , Issue.2 , pp. 114-121
    • Zheng, W.1    Lee, D.H.2    Shi, Q.3
  • 20
    • 84861893114 scopus 로고    scopus 로고
    • Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg Marquardt algorithm
    • Jun. 2012
    • K. Y. Chan, T. S. Dillon, J. Singh, and E. Chang, Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg Marquardt algorithm, IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 644-654, Jun. 2012.
    • IEEE Trans. Intell. Transp. Syst. , vol.13 , Issue.2 , pp. 644-654
    • Chan, K.Y.1    Dillon, T.S.2    Singh, J.3    Chang, E.4
  • 21
  • 22
    • 4444369422 scopus 로고    scopus 로고
    • Short-term traffic forecasting: Overview of objectives and methods
    • Sep. 2004
    • E. I. Vlahogianni, J. C. Golias, and M. G. Karlaftis, Short-term traffic forecasting: Overview of objectives and methods, Transp. Rev., vol. 24, no. 5, pp. 533-557, Sep. 2004.
    • Transp. Rev. , vol.24 , Issue.5 , pp. 533-557
    • Vlahogianni, E.I.1    Golias, J.C.2    Karlaftis, M.G.3
  • 23
    • 0031472064 scopus 로고    scopus 로고
    • Traffic flow forecasting: Comparison of modeling approaches
    • Jul. 1997
    • B. L. Smith and M. J. Demetsky, Traffic flow forecasting: Comparison of modeling approaches, J. Transp. Eng., vol. 123, no. 4, pp. 261-266, Jul. 1997.
    • J. Transp. Eng. , vol.123 , Issue.4 , pp. 261-266
    • Smith, B.L.1    Demetsky, M.J.2
  • 24
    • 33645764663 scopus 로고    scopus 로고
    • Developing multi-regime speed-density relationships using cluster analysis
    • Jan. 2005
    • L. Sun and J. Zhou, Developing multi-regime speed-density relationships using cluster analysis, Transp. Res. Rec., J. Transp. Res. Board, vol. 1934, no. 1, pp. 64-71, Jan. 2005.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1934 , Issue.1 , pp. 64-71
    • Sun, L.1    Zhou, J.2
  • 25
    • 77953362241 scopus 로고    scopus 로고
    • Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions
    • Oct. 2010
    • Y. Kamarianakis, H. O. Gao, and P. Prastacos, Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions, Transp. Res. C, Emerg. Technol., vol. 18, no. 5, pp. 821-840, Oct. 2010.
    • Transp. Res. C, Emerg. Technol. , vol.18 , Issue.5 , pp. 821-840
    • Kamarianakis, Y.1    Gao, H.O.2    Prastacos, P.3
  • 26
    • 0036974476 scopus 로고    scopus 로고
    • Freeway travel time prediction with state-space neural networks modeling state-space dynamics with recurrent neural networks
    • Jan. 2002
    • J.W. C. V. Lint, S. P. Hoogendoorn, and H. J. Van Zuylen, Freeway travel time prediction with state-space neural networks modeling state-space dynamics with recurrent neural networks, Transp. Res. Rec., J. Transp. Res. Board, vol. 1811, no. 1, pp. 30-39, Jan. 2002.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1811 , Issue.1 , pp. 30-39
    • Lint, J.W.C.V.1    Hoogendoorn, S.P.2    Van Zuylen, H.J.3
  • 27
    • 4544274995 scopus 로고    scopus 로고
    • A simple and effective method for predicting travel times on freeways
    • Sep. 2004
    • E. V. Zwet and J. Rice, A simple and effective method for predicting travel times on freeways, IEEE Trans. Intell. Transp. Syst., vol. 5, no. 3, pp. 200-207, Sep. 2004.
    • IEEE Trans. Intell. Transp. Syst. , vol.5 , Issue.3 , pp. 200-207
    • Zwet, E.V.1    Rice, J.2
  • 28
    • 0042664086 scopus 로고    scopus 로고
    • Short term travel time predicting using a time-varying coefficient linear model
    • Jun./Aug. 2003
    • X. Zhang and J. A. Rice, Short term travel time predicting using a time-varying coefficient linear model, Transp. Res. C, Emerg. Technol., vol. 11, no. 3/4, pp. 187-210, Jun./Aug. 2003.
    • Transp. Res. C, Emerg. Technol. , vol.11 , Issue.3-4 , pp. 187-210
    • Zhang, X.1    Rice, J.A.2
  • 29
    • 0034432731 scopus 로고    scopus 로고
    • Estimation and application of dynamic speed-density relations by using transfer function models
    • Jan. 2000
    • H. Tavana and H. Mahmassani, Estimation and application of dynamic speed-density relations by using transfer function models, Transp. Res. Rec., J. Transp. Res. Board, vol. 1710, no. 1, pp. 47-57, Jan. 2000.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1710 , Issue.1 , pp. 47-57
    • Tavana, H.1    Mahmassani, H.2
  • 30
    • 0036954384 scopus 로고    scopus 로고
    • Adaptive speed estimation using transfer function models for real-time dynamic traffic assignment operation
    • Jan. 2002
    • N. Huynh, H. S. Mahmassani, and H. Tavana, Adaptive speed estimation using transfer function models for real-time dynamic traffic assignment operation, Transp. Res. Rec., J. Transp. Res. Board, vol. 1783, no. 1, pp. 55-65, Jan. 2002.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1783 , Issue.1 , pp. 55-65
    • Huynh, N.1    Mahmassani, H.S.2    Tavana, H.3
  • 31
    • 15544383029 scopus 로고    scopus 로고
    • Adaptive calibration of dynamic speed-density relations for online network traffic estimation and prediction applications
    • Jan. 2004
    • X. Qin and H. S. Mahmassani, Adaptive calibration of dynamic speed-density relations for online network traffic estimation and prediction applications, Transp. Res. Rec., J. Transp. Res. Board, vol. 1876, no. 1, pp. 82-89, Jan. 2004.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1876 , Issue.1 , pp. 82-89
    • Qin, X.1    Mahmassani, H.S.2
  • 32
    • 33847119831 scopus 로고    scopus 로고
    • Short-term traffic flow prediction with regime switching models
    • Jan. 2006
    • M. Cetin and G. Comert, Short-term traffic flow prediction with regime switching models, Transp. Res. Rec., J. Transp. Res. Board, vol. 1965, no. 1, pp. 23-31, Jan. 2006.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1965 , Issue.1 , pp. 23-31
    • Cetin, M.1    Comert, G.2
  • 34
    • 45849138568 scopus 로고    scopus 로고
    • Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow
    • Oct. 2008
    • L. Dimitriou, T. Tsekeris, and A. Stathopoulos, Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow, Transp. Res. C, Emerg. Technol., vol. 16, no. 5, pp. 554-573, Oct. 2008.
    • Transp. Res. C, Emerg. Technol. , vol.16 , Issue.5 , pp. 554-573
    • Dimitriou, L.1    Tsekeris, T.2    Stathopoulos, A.3
  • 35
    • 79952736659 scopus 로고    scopus 로고
    • Real-time road traffic prediction with spatiotemporal correlations
    • Aug. 2011
    • W. Min and L. Wynter, Real-time road traffic prediction with spatiotemporal correlations, Transp. Res. C, Emerg. Technol., vol. 19, no. 4, pp. 606-616, Aug. 2011.
    • Transp. Res. C, Emerg. Technol. , vol.19 , Issue.4 , pp. 606-616
    • Min, W.1    Wynter, L.2
  • 36
    • 61849156325 scopus 로고    scopus 로고
    • An aggregation approach to short-term traffic flow prediction
    • Mar. 2009
    • M. Tan, S. C.Wong, J. M. Xu, Z. R. Guan, and P. Zhang, An aggregation approach to short-term traffic flow prediction, IEEE Trans. Intell. Transp. Syst., vol. 10, no. 1, pp. 60-69, Mar. 2009.
    • IEEE Trans. Intell. Transp. Syst. , vol.10 , Issue.1 , pp. 60-69
    • Tan, M.1    Wong, S.C.2    Xu, J.M.3    Guan, Z.R.4    Zhang, P.5
  • 37
    • 38349078518 scopus 로고    scopus 로고
    • Composite nearest neighbor nonparametric regression to improve traffic prediction
    • Jan. 2007
    • M. D. Kindzerske and D. Ni, Composite nearest neighbor nonparametric regression to improve traffic prediction, Transp. Res. Rec., J. Transp. Res. Board, vol. 1993, no. 1, pp. 30-35, Jan. 2007.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1993 , Issue.1 , pp. 30-35
    • Kindzerske, M.D.1    Ni, D.2
  • 38
    • 33645673658 scopus 로고    scopus 로고
    • Modeling traffic volatility dynamics in an urban network
    • Jan. 2005
    • Y. A. Kamarianakis and P. P. Kanas, Modeling traffic volatility dynamics in an urban network, Transp. Res. Rec., J. Transp. Res. Board, vol. 1923, no. 1, pp. 18-27, Jan. 2005.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.1923 , Issue.1 , pp. 18-27
    • Kamarianakis, Y.A.1    Kanas, P.P.2
  • 39
    • 78651271213 scopus 로고    scopus 로고
    • Real-time short term traffic speed level forecasting and uncertainty quantification using layered Kalman filters
    • Dec. 2010
    • J. Guo and B. M. Williams, Real-time short term traffic speed level forecasting and uncertainty quantification using layered Kalman filters, Transp. Res. Rec., J. Transp. Res. Board, vol. 2175, no. 1, pp. 28-37, Dec. 2010.
    • Transp. Res. Rec., J. Transp. Res. Board , vol.2175 , Issue.1 , pp. 28-37
    • Guo, J.1    Williams, B.M.2
  • 40
    • 67349277727 scopus 로고    scopus 로고
    • Memory properties and fractional integration in transportation time-series
    • Aug. 2009
    • M. G. Karlaftis and E. I. Vlahogianni, Memory properties and fractional integration in transportation time-series, Transp. Res. C, Emerg. Technol., vol. 17, no. 4, pp. 444-453, Aug. 2009.
    • Transp. Res. C, Emerg. Technol. , vol.17 , Issue.4 , pp. 444-453
    • Karlaftis, M.G.1    Vlahogianni, E.I.2
  • 41
    • 21444436443 scopus 로고    scopus 로고
    • Intercept correction and structural change
    • Sep. 1996
    • M. P. Clements and D. F. Hendry, Intercept correction and structural change, J. Appl. Econom., vol. 11, no. 5, pp. 475-494, Sep. 1996.
    • J. Appl. Econom. , vol.11 , Issue.5 , pp. 475-494
    • Clements, M.P.1    Hendry, D.F.2
  • 42
    • 84885188335 scopus 로고    scopus 로고
    • Forecasting from misspecified models in the presence of unanticipated location shifts
    • M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press ch. 11
    • M. P. Clements and D. F. Hendry, Forecasting from misspecified models in the presence of unanticipated location shifts, in The Oxford Handbook of Economic Forecasting, M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press, 2011, ch. 11, pp. 315-355.
    • (2011) The Oxford Handbook of Economic Forecasting , pp. 315-355
    • Clements, M.P.1    Hendry, D.F.2
  • 43
    • 84886726819 scopus 로고    scopus 로고
    • Forecasting breaks and forecasting during breaks
    • M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press ch. 10
    • J. L. Castle, N. W. Fawcett, and D. F. Hendry, Forecasting breaks and forecasting during breaks, in The Oxford Handbook of Economic Forecasting, M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press, 2011, ch. 10, pp. 271-315.
    • (2011) The Oxford Handbook of Economic Forecasting , pp. 271-315
    • Castle, J.L.1    Fawcett, N.W.2    Hendry, D.F.3
  • 44
    • 84865132582 scopus 로고    scopus 로고
    • Forecast combinations
    • M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press ch. 12
    • M. Aiolfi, C. Capistran, and A. Timmermann, Forecast combinations, in The Oxford Handbook of Economic Forecasting, M. P. Clements and D. F. Hendry, Eds. London, U.K.: Oxford Univ. Press, 2011, ch. 12, pp. 355-391.
    • (2011) The Oxford Handbook of Economic Forecasting , pp. 355-391
    • Aiolfi, M.1    Capistran, C.2    Timmermann, A.3
  • 45
    • 80052152529 scopus 로고    scopus 로고
    • Calling recessions in real time
    • Oct.-Dec. 2010
    • J. D. Hamilton, Calling recessions in real time, Int. J. Forecast., vol. 27, no. 4, pp. 1006-1026, Oct.-Dec. 2010.
    • Int. J. Forecast. , vol.27 , Issue.4 , pp. 1006-1026
    • Hamilton, J.D.1
  • 46
    • 68149114028 scopus 로고    scopus 로고
    • 2nd ed. Basingstoke, U.K.: Palgrave McMillan
    • J. D. Hamilton, Regime-Switching Models, 2nd ed. Basingstoke, U.K.: Palgrave McMillan, 2008.
    • (2008) Regime-Switching Models
    • Hamilton, J.D.1
  • 47
    • 24344503821 scopus 로고    scopus 로고
    • An objective Bayesian analysis of the change point problem
    • Aug. 2005
    • E. Moreno, G. Casella, and A. Garcia-Ferrer, An objective Bayesian analysis of the change point problem, Stoch. Environ. Res. Risk Assess., vol. 19, no. 3, pp. 191-204, Aug. 2005.
    • Stoch. Environ. Res. Risk Assess. , vol.19 , Issue.3 , pp. 191-204
    • Moreno, E.1    Casella, G.2    Garcia-Ferrer, A.3
  • 48
    • 68649087032 scopus 로고    scopus 로고
    • Objective Bayesian analysis of multiple change points for linear models
    • F. J. Giron, E. Moreno, and G. Casella, Objective Bayesian analysis of multiple change points for linear models, in Proc. Bayesian Stats., 2007, vol. 8, pp. 1-27.
    • (2007) Proc. Bayesian Stats. , vol.8 , pp. 1-27
    • Giron, F.J.1    Moreno, E.2    Casella, G.3
  • 49
    • 77952840924 scopus 로고    scopus 로고
    • A sequential smoothing algorithm with linear computational cost
    • Jun. 2010
    • P. Fearnhead, D. Wyncoll, and J. Tawn, A sequential smoothing algorithm with linear computational cost, Biometrika, vol. 97, no. 2, pp. 447- 464, Jun. 2010.
    • Biometrika , vol.97 , Issue.2 , pp. 447-464
    • Fearnhead, P.1    Wyncoll, D.2    Tawn, J.3
  • 50
    • 0036749135 scopus 로고    scopus 로고
    • Detection of undocumented change points: A revision of the two-phase regression model
    • Sep. 2002
    • R. Lund and J. Reeves, Detection of undocumented change points: A revision of the two-phase regression model, J. Climate, vol. 15, no. 17, pp. 2547-2554, Sep. 2002.
    • J. Climate , vol.15 , Issue.17 , pp. 2547-2554
    • Lund, R.1    Reeves, J.2
  • 51
    • 0037207305 scopus 로고    scopus 로고
    • Detection of onset of neuronal activity by allowing for heterogeneity in the change points
    • Dec. 2002
    • Y. Ritov, A. Raz, and H. Bergman, Detection of onset of neuronal activity by allowing for heterogeneity in the change points, J. Neurosci. Methods, vol. 122, no. 1, pp. 25-42, Dec. 2002.
    • J. Neurosci. Methods , vol.122 , Issue.1 , pp. 25-42
    • Ritov, Y.1    Raz, A.2    Bergman, H.3
  • 52
    • 33846809173 scopus 로고    scopus 로고
    • A review of homogenization techniques for climate data and their applicability to precipitation series
    • Feb. 2007
    • C. Beaulieu, T. B. M. J. Ouarda, and O. Seidou, A review of homogenization techniques for climate data and their applicability to precipitation series, Hydrol. Sci. J.-J. Sci. Hyrolog., vol. 52, pp. 18-37, Feb. 2007.
    • Hydrol. Sci. J.-J. Sci. Hyrolog. , vol.52 , pp. 18-37
    • Beaulieu, C.1    Ouarda, T.B.M.J.2    Seidou, O.3
  • 53
    • 84857359596 scopus 로고    scopus 로고
    • Bayesian change point detection for satellite fault prediction
    • Cambridge, U.K.
    • R. Turner, Bayesian change point detection for satellite fault prediction, in Proc. IGC, Cambridge, U.K., 2010, vol. 1, pp. 213-221.
    • (2010) Proc. IGC , vol.1 , pp. 213-221
    • Turner, R.1
  • 54
    • 77956553642 scopus 로고    scopus 로고
    • Gaussian process change point models
    • Haifa, Israel, Jun. 2010
    • Y. Saatci, R. Turner, and C. E. Rasmussen, Gaussian process change point models, in Proc. Int. Conf. Mach. Learn., Haifa, Israel, Jun. 2010, vol. 1, pp. 927-934.
    • Proc. Int. Conf. Mach. Learn. , vol.1 , pp. 927-934
    • Saatci, Y.1    Turner, R.2    Rasmussen, C.E.3
  • 55
    • 84893439978 scopus 로고    scopus 로고
    • Adaptive sequential Bayesian change point detection
    • Workshop NIPS, Whistler, BC, Canada Dec. 2009
    • R. Turner, Y. Saatci, and C. E. Rasmussen, Adaptive sequential Bayesian change point detection, in Proc. Temp. Segment. Workshop NIPS, Whistler, BC, Canada, Dec. 2009, vol. 1, pp. 1-4.
    • Proc. Temp. Segment. , vol.1 , pp. 1-4
    • Turner, R.1    Saatci, Y.2    Rasmussen, C.E.3
  • 57
    • 78149279501 scopus 로고    scopus 로고
    • Sequential Bayesian prediction in the presence of change points and faults
    • Nov. 2010
    • R. Garnett, M. Osborne, S. Reece, A. Rogers, and S. Roberts, Sequential Bayesian prediction in the presence of change points and faults, Comput. J., vol. 53, no. 9, pp. 1430-1446, Nov. 2010.
    • Comput. J. , vol.53 , Issue.9 , pp. 1430-1446
    • Garnett, R.1    Osborne, M.2    Reece, S.3    Rogers, A.4    Roberts, S.5
  • 58
    • 53549114249 scopus 로고    scopus 로고
    • Selecting hidden Markov model state number with cross-validated likelihood
    • Oct. 2008
    • G. Celeux and J. B. Durand, Selecting hidden Markov model state number with cross-validated likelihood, Comput. Stat., vol. 23, no. 4, pp. 541-564, Oct. 2008.
    • Comput. Stat. , vol.23 , Issue.4 , pp. 541-564
    • Celeux, G.1    Durand, J.B.2
  • 59
    • 0024610919 scopus 로고    scopus 로고
    • A tutorial on hidden Markov models and selected applications in speech recognition
    • Feb. 1989
    • L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
    • Proc. IEEE , vol.77 , Issue.2 , pp. 257-286
    • Rabiner, L.1
  • 60
    • 84965063004 scopus 로고    scopus 로고
    • An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
    • May. 1967
    • L. Baum and J. Eagon, An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology, Bull. Amer. Math. Soc., vol. 73, no. 3, pp. 360-363, May 1967.
    • Bull. Amer. Math. Soc. , vol.73 , Issue.3 , pp. 360-363
    • Baum, L.1    Eagon, J.2
  • 61
    • 0000353178 scopus 로고    scopus 로고
    • A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains
    • Feb. 1970
    • L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains, Ann. Math. Stat., vol. 41, no. 1, pp. 164-171, Feb. 1970.
    • Ann. Math. Stat. , vol.41 , Issue.1 , pp. 164-171
    • Baum, L.E.1    Petrie, T.2    Soules, G.3    Weiss, N.4
  • 62
    • 0002629270 scopus 로고    scopus 로고
    • Maximum likelihood from incomplete data via the em algorithm
    • Jan. 1977
    • A. P. Dempster, N.M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Stat. Soc., B, vol. 39, no. 1, pp. 1-38, Jan. 1977.
    • J. Roy. Stat. Soc., B , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 65
    • 84857644454 scopus 로고    scopus 로고
    • Initializing the em algorithm in Gaussian mixture models with an unknown number of components
    • Jun. 2012
    • V. Melnykov and I. Melnykov, Initializing the EM algorithm in Gaussian mixture models with an unknown number of components, Comput. Stat. Data Anal., vol. 56, no. 6, pp. 1381-1395, Jun. 2012.
    • Comput. Stat. Data Anal. , vol.56 , Issue.6 , pp. 1381-1395
    • Melnykov, V.1    Melnykov, I.2
  • 66
    • 0001393743 scopus 로고    scopus 로고
    • An approach to the probability distribution of CUSUM run length
    • Dec. 1972
    • D. Brook and D. A. Evans, An approach to the probability distribution of CUSUM run length, Biometrika, vol. 59, no. 3, pp. 539-549, Dec. 1972.
    • Biometrika , vol.59 , Issue.3 , pp. 539-549
    • Brook, D.1    Evans, D.A.2
  • 67
    • 0035563672 scopus 로고    scopus 로고
    • Multivariate vehicular traffic flow prediction: Evaluation of ARIMAX modeling
    • 01-3488
    • B. M.Williams, Multivariate vehicular traffic flow prediction: Evaluation of ARMAX modeling, in Proc. Transp. Res. Rec., 2001, vol. 1776, pp. 194-200. (Pubitemid 34404280)
    • (2001) Transportation Research Record , Issue.1776 , pp. 194-200
    • Williams, B.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.