Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization

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References

  • Basu S , Michailidis G (2015) Regularized estimation in sparse high-dimensional time series models. Ann. Statist. 43(4):1535–1567.CrossrefGoogle Scholar
  • Basu S , Li X , Michailidis G (2019) Low rank and structured modeling of high-dimensional vector autoregressions. IEEE Trans. Signal Process. 67(5):1207–1222.CrossrefGoogle Scholar
  • Carriero A , Kapetanios G , Marcellino M (2016) Structural analysis with multivariate autoregressive index models. J. Econometrics 192(2):332–348.CrossrefGoogle Scholar
  • Che Z , Purushotham S , Cho K , Sontag D , Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1):1–12.CrossrefGoogle Scholar
  • Chen X , Sun L (2022) Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(9):4659–4673.Google Scholar
  • Cheng Z , Trépanier M , Sun L (2022) Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition. Transportation Sci. 56(4):904–918.LinkGoogle Scholar
  • Chi Y , Lu YM , Chen Y (2019) Nonconvex optimization meets low-rank matrix factorization: An overview. IEEE Trans. Signal Process. 67(20):5239–5269.CrossrefGoogle Scholar
  • Dunlavy DM , Kolda TG , Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowl. Discov. Data 5(2):1–27.CrossrefGoogle Scholar
  • Forni M , Hallin M , Lippi M , Reichlin L (2000) The generalized dynamic-factor model: Identification and estimation. Rev. Econom. Statist. 82(4):540–554.CrossrefGoogle Scholar
  • Golub GH , Van Loan CF (2013) Matrix Computations , 4th ed. (The Johns Hopkins University Press, Baltimore).CrossrefGoogle Scholar
  • Guerrero-Ibáñez J , Zeadally S , Contreras-Castillo J (2018) Sensor technologies for intelligent transportation systems. Sensors 18(4):1212.CrossrefGoogle Scholar
  • Gultekin S , Paisley J (2018) Online forecasting matrix factorization. IEEE Trans. Signal Process. 67(5):1223–1236.CrossrefGoogle Scholar
  • Han F , Lu H , Liu H (2015) A direct estimation of high dimensional stationary vector autoregressions. J. Mach. Learn. Res. 16(97):3115–3150.Google Scholar
  • Hangos KM , Bokor J , Szederkényi G (2006) Analysis and Control of Nonlinear Process Systems (Springer Science & Business Media, Berlin).Google Scholar
  • Jain NK , Saini R , Mittal P (2019) A review on traffic monitoring system techniques. Soft Comput. Theories Appl. Proc. SoCTA 2017 , 569–577.Google Scholar
  • Janecek A , Valerio D , Hummel KA , Ricciato F , Hlavacs H (2015) The cellular network as a sensor: From mobile phone data to real-time road traffic monitoring. IEEE Trans. Intell. Transport. Syst. 16(5):2551–2572.CrossrefGoogle Scholar
  • Kawabata K , Bhatia S , Liu R , Wadhwa M , Hooi B (2021) SSMF: Shifting seasonal matrix factorization. Adv. Neural Inform. Processing Systems 34:3863–3873.Google Scholar
  • Koop G , Korobilis D , Pettenuzzo D (2019) Bayesian compressed vector autoregressions. J. Econometrics 210(1):135–154.CrossrefGoogle Scholar
  • Koren Y , Bell R , Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37.CrossrefGoogle Scholar
  • LeCun Y , Bengio Y , Hinton G (2015) Deep learning. Nature 521(7553):436–444.CrossrefGoogle Scholar
  • Liu G (2022) Time series forecasting via learning convolutionally low-rank models. IEEE Trans. Inform. Theory 68(5):3362–3380.CrossrefGoogle Scholar
  • Liu G , Zhang W (2022) Recovery of future data via convolution nuclear norm minimization. IEEE Trans. Inform. Theory 69(1):650–665.CrossrefGoogle Scholar
  • Lütkepohl H (2013) Introduction to Multiple Time Series Analysis (Springer Science & Business Media, Berlin).Google Scholar
  • Prince SJ (2023) Understanding Deep Learning (MIT Press, Cambridge, MA).Google Scholar
  • Rao N , Yu HF , Ravikumar P , Dhillon IS (2015) Collaborative filtering with graph information: Consistency and scalable methods. NIPS , vol. 2 (Citeseer), 7.Google Scholar
  • Stock JH , Watson MW (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. Handbook of Macroeconomics , vol. 2 (Elsevier, Amsterdam), 415–525.Google Scholar
  • Takeuchi K , Kashima H , Ueda N (2017) Autoregressive tensor factorization for spatio-temporal predictions. 2017 IEEE Internat. Conf. Data Mining (ICDM) (IEEE), 1105–1110.Google Scholar
  • Treiber M , Kesting A (2013) Traffic flow dynamics. Traffic Flow Dynamics: Data, Models and Simulation (Springer-Verlag, Berlin, Heidelberg), 983–1000.CrossrefGoogle Scholar
  • Velu R , Reinsel GC (1998) Multivariate Reduced-Rank Regression: Theory and Applications , vol. 136 (Springer Science & Business Media, Berlin).Google Scholar
  • Velu RP , Reinsel GC , Wichern DW (1986) Reduced rank models for multiple time series. Biometrika 73(1):105–118.CrossrefGoogle Scholar
  • Verleysen M , François D (2005) The curse of dimensionality in data mining and time series prediction. Internat. Work Conf. Artificial Neural Networks (Springer, New York), 758–770.Google Scholar
  • Wang D , Zheng Y , Lian H , Li G (2022) High-dimensional vector autoregressive time series modeling via tensor decomposition. J. Amer. Statist. Assoc. 117(539):1338–1356.CrossrefGoogle Scholar
  • Xiong L , Chen X , Huang TK , Schneider J , Carbonell JG (2010) Temporal collaborative filtering with Bayesian probabilistic tensor factorization. Proc. 2010 SIAM Internat. Conf. Data Mining (SIAM), 211–222.Google Scholar
  • Yu HF , Rao N , Dhillon IS (2016) Temporal regularized matrix factorization for high-dimensional time series prediction. Adv. Neural Inform. Processing Systems 29:847–855.Google Scholar
  • Zheng Y (2015) Trajectory data mining: An overview. ACM Trans. Intell. Syst. Technol. 6(3):1–41.CrossrefGoogle Scholar
  • Zheng F , Jabari SE , Liu HX , Lin D (2018) Traffic state estimation using stochastic Lagrangian dynamics. Transportation Res. Part B Methodological 115:143–165.CrossrefGoogle Scholar
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