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Data Science, Quality & Reliability

A Markov-switching hidden heterogeneous network autoregressive model for multivariate time series data with multimodality

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Pages 1118-1132 | Received 10 May 2022, Accepted 30 Oct 2022, Published online: 06 Jan 2023

References

  • Aasnaes, H. and Kailath, T. (1973) An innovations approach to least-squares estimation–part vii: Some applications of vector autoregressive-moving average models. IEEE Transactions on Automatic Control, 18(6), 601–607.
  • Alexandra von Meier, R.A. (2017) Every Moment Counts: Synchrophasors for Distribution Networks With Variable Resources (Second ed.). Academic Press, Boston, MA, pp.435–444.
  • Aquaro, M., Bailey, N. and Pesaran, M.H. (2021) Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices. Journal of Applied Econometrics, 36(1), 18–44.
  • Baltagi, B.H., Song, S.H. and Koh, W. (2003) Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117(1), 123–150.
  • Baum, L.E. and Petrie, T. (1966) Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics, 37(6), 1554–1563.
  • Biem, A. (2003) A model selection criterion for classification: Application to hmm topology optimization, in Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., Volume 1, IEEE Press, Piscataway, NJ, pp. 104–108.
  • Chen, X., Irie, K., Banks, D., Haslinger, R., Thomas, J. and West, M. (2018) Scalable Bayesian modeling, monitoring, and analysis of dynamic network flow data. Journal of the American Statistical Association, 113(522), 519–533.
  • Chen, Z., Li, Y., Xia, T. and Pan, E. (2019) Hidden Markov model with auto-correlated observations for remaining useful life prediction and optimal maintenance policy. Reliability Engineering & System Safety, 184, 123–136.
  • Cholette, M.E. and Djurdjanovic, D. (2014) Degradation modeling and monitoring of machines using operation-specific hidden Markov models. IIE Transactions, 46(10), 1107–1123.
  • Dong, W., Lepri, B. and Pentland, A.S. (2011) Modeling the co-evolution of behaviors and social relationships using mobile phone data, in Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia, Beijing, China pp. 134–143, Association for Computing Machinery, New York, NY.
  • Dong, W., Pentland, A.S. and Heller, K.A. (2012) Graph-coupled hmms for modeling the spread of infection, in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI’12, pp. 227–236, Catalina Island, CA.
  • Dueker, M.J., Psaradakis, Z., Sola, M. and Spagnolo, F. (2011) Multivariate contemporaneous-threshold autoregressive models. Journal of Econometrics, 160(2), 311–325.
  • Ehrmann, M., Ellison, M. and Valla, N. (2003) Regime-dependent impulse response functions in a Markov-switching vector autoregression model. Economics Letters, 78(3), 295–299.
  • Fong, P.W., Li, W.K., Yau, C. and Wong, C.S. (2007) On a mixture vector autoregressive model. Canadian Journal of Statistics, 35(1), 135–150.
  • Hu, M., Zheng, H., Wang, W. and Guo, Y. (2017) Estimate health condition of power supply at base-station sites based on alarm data, in 7th IET International Conference on Wireless, Mobile Multimedia Networks (ICWMMN 2017), IEEE Press, Piscataway, NJ, pp. 7–12.
  • Hubert, L. and Arabie, P. (1985) Comparing partitions. Journal of Classification, 2(1), 193–218.
  • Irie, K., Glynn, C. and Aktekin, T. (2022) Sequential modeling, monitoring, and forecasting of streaming web traffic data. The Annals of Applied Statistics, 16(1), 300–325.
  • Jain, G. and Prasad, R.R. (2020) Machine learning, prophet and xgboost algorithm: Analysis of traffic forecasting in telecom networks with time series data, in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), IEEE Press, Piscataway, NJ, pp. 893–897.
  • Kalliovirta, L., Meitz, M. and Saikkonen, P. (2016) Gaussian mixture vector autoregression. Journal of Econometrics 192(2), 485–498.
  • Keisuke, K. (2022) Spatial dependence in regional business cycles: Evidence from Mexican states. Journal of Spatial Econometricsm 3(1), 1.
  • Knight, M.I., Nunes, M.A. and Nason, G.P. (2016) Modelling, detrending and decorrelation of network time series. Available online: https://arxiv.org/abs/1603.03221 (accessed 25 September 2022).
  • Le, N.D., Martin, R.D. and Raftery, A.E. (1996) Modeling flat stretches, bursts outliers in time series using mixture transition distribution models. Journal of the American Statistical Association, 91(436), 1504–1515.
  • Lee, L.-F. and Yu, J. (2010) Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165–185.
  • LeSage, J.P. and Pace, R.K. (2010) Spatial Econometric Models, Springer, Berlin Heidelberg, pp. 355–376.
  • Li, Z. and Xiao, H. (2021) Multi-linear tensor autoregressive models. Available online: https://arxiv.org/abs/2110.00928 (accessed 25 September 2022).
  • Maruotti, A. (2011) Mixed hidden Markov models for longitudinal data: An overview. International Statistical Review, 79(3), 427–454.
  • Matias, C. and Miele, V. (2017) Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 1119–1141.
  • Ruchjana, B.N., Borovkova, S.A. and Lopuhaa, H. (2012) Least squares estimation of generalized space time autoregressive (gstar) model and its properties, in AIP Conference Proceedings, Volume 1450, pp. 61–64, American Institute of Physics, Bandung, Indonesia.
  • Schwarz, G. (1978, July) Estimating the dimension of a model. Annals of Statistics. 6(2), 461–464.
  • Shen, J. and Cui, L. (2017) Reliability performance for dynamic multi-state repairable systems with k regimes. IISE Transactions, 49(9), 911–926.
  • Wang, D., Li, F. and Liu, K. (2021) Modeling and monitoring of a multivariate spatio-temporal network system. IISE Transactions. Available online: https://doi.org/10.1080/24725854.2021.1973157 (accessed 25 September 2022).
  • Wang, X., Lebarbier, E., Aubert, J. and Robin, S. (2019) Variational inference for coupled hidden Markov models applied to the joint detection of copy number variations. The International Journal of Biostatistics, 15(1), 20180023.
  • Yang, T., Chi, Y., Zhu, S., Gong, Y. and Jin, R. (2011) Detecting communities and their evolutions in dynamic social networks—a Bayesian approach. Machine Learning, 82(2), 157–189.
  • Zhu, X. and Pan, R. (2020) Grouped network vector autoregression. Statistica Sinica, 30, 1437–1462.
  • Zhu, X., Pan, R., Li, G., Liu, Y. and Wang, H. (2017) Network vector autoregression. The Annals of Statistics 45(3), 1096–1123.
  • Zou, N. and Li, J. (2017) Modeling and change detection of dynamic network data by a network state space model. IISE Transactions, 49(1), 45–57.

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