Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization
References
- (2015) Online time series prediction with missing data. Internat. Conf. Machine Learn. (Curran Associates, Red Hook, NY), 2191–2199.Google Scholar
- (2019) Fast and provable algorithms for spectrally sparse signal reconstruction via low-rank Hankel matrix completion. Appl. Comput. Harmonic Anal. 46(1):94–121.Crossref, Google Scholar
- (2018) Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1):1–12.Crossref, Google Scholar
- (2018) Harnessing structures in big data via guaranteed low-rank matrix estimation: Recent theory and fast algorithms via convex and nonconvex optimization. IEEE Signal Processing Magazine 35(4):14–31.Crossref, Google Scholar
- (2021) Bayesian temporal factorization for multidimensional time series prediction. IEEE Trans. Pattern Anal. Machine Intelligence 44(9):4659–4673.Google Scholar
- (2021) Exact matrix completion based on low rank Hankel structure in the Fourier domain. Appl. Comput. Harmonic Anal. 55:149–184.Crossref, Google Scholar
- (2019) A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Res. Part C Emerging Tech. 98:73–84.Crossref, Google Scholar
- (2020) A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation. Transportation Res. Part C Emerging Tech. 117:102673.Crossref, Google Scholar
- (2024) Forecasting urban traffic states with sparse data using Hankel temporal matrix factorization. http://dx.doi.org/10.1287/ijoc.2022.0197.cd, https://github.com/INFORMSJoC/2022.0197.Google Scholar
- (2022) Nonstationary temporal matrix factorization for multivariate time series forecasting. Preprint, submitted March 20, https://arxiv.org/abs/2203.10651.Google Scholar
- (2019) Nonconvex optimization meets low-rank matrix factorization: An overview. IEEE Trans. Signal Processing 67(20):5239–5269.Crossref, Google Scholar
- (2013) Ridesharing: The state-of-the-art and future directions. Transportation Res. Part B Methodological 57:28–46.Crossref, Google Scholar
- (2013) Matrix Computations, 4th ed. (The Johns Hopkins University Press, Baltimore).Crossref, Google Scholar
- (2018) Online forecasting matrix factorization. IEEE Trans. Signal Processing 67(5):1223–1236.Crossref, Google Scholar
- (1994) Time Series Analysis (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
- (2022) DeepETA: How Uber predicts arrival times using deep learning. Accessed October 26, 2023, https://www.uber.com/en-HK/blog/deepeta-how-uber-predicts-arrival-times/.Google Scholar
- (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37.Crossref, Google Scholar
- (2013) Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transportation Res. Part C Emerging Tech. 34:108–120.Crossref, Google Scholar
- (2021) Time-series forecasting with deep learning: A survey. Philos. Trans. Roy. Soc. A 379(2194):20200209.Crossref, Google Scholar
- (2022) Time series forecasting via learning convolutionally low-rank models. IEEE Trans. Inform. Theory 68(5):3362–3380.Crossref, Google Scholar
- (2022) Recovery of future data via convolution nuclear norm minimization. IEEE Trans. Inform. Theory 69(1):650–665.Crossref, Google Scholar
- (2022) Unsupervised learning for human mobility behaviors. INFORMS J. Comput. 34(3):1565–1586.Link, Google Scholar
- (2007) Probabilistic matrix factorization. Advances in Neural Information Processing Systems, vol. 20 (Curran Associates, Red Hook, NY), 1257–1264.Google Scholar
- (2021) Network-wide traffic states imputation using self-interested coalitional learning. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (ACM, New York), 1370–1378.Google Scholar
- (2015) Collaborative filtering with graph information: Consistency and scalable methods. Advances in Neural Information Processing Systems, vol. 28 (Curran Associates, Red Hook, NY), 2107–2115.Google Scholar
- (2018) Multi-output Gaussian processes for crowdsourced traffic data imputation. IEEE Trans. Intelligent Transportation Systems 20(2):594–603.Crossref, Google Scholar
- (2020) Matrix and tensor completion in multiway delay embedded space using tensor train, with application to signal reconstruction. IEEE Signal Processing Lett. 27:810–814.Crossref, Google Scholar
- (2019) Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates, Red Hook, NY), 4838–4847.Google Scholar
- (2020) Block Hankel tensor ARIMA for multiple short time series forecasting. Proc. Conf. AAAI Artificial Intelligence, vol. 34(4) (AAAI Press, Palo Alto, CA), 5758–5766.Google Scholar
- (2020) Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values. Proc. Conf. AAAI Artificial Intelligence, vol. 34(4) (AAAI Press, Palo Alto, CA), 5956–5963.Google Scholar
- (2013) Traffic Flow Dynamics: Data, Models and Simulation (Springer-Verlag, Berlin, Heidelberg), 983–1000.Google Scholar
- (2010) Temporal collaborative filtering with Bayesian probabilistic tensor factorization. Proc. 2010 SIAM Internat. Conf. Data Mining (SIAM, Philadelphia), 211–222.Google Scholar
- (2018) Missing slice recovery for tensors using a low-rank model in embedded space. Proc. IEEE Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 8251–8259.Google Scholar
- (2016) Temporal regularized matrix factorization for high-dimensional time series prediction. Advances in Neural Information Processing Systems, vol. 29 (Curran Associates, Red Hook, NY), 847–855.Google Scholar
- (2019) Correction of corrupted columns through fast robust Hankel matrix completion. IEEE Trans. Signal Processing 67(10):2580–2594.Crossref, Google Scholar
- (2021) Dynamic tensor recommender systems. J. Machine Learn. Res. 22(1):3032–3066.Google Scholar
- (2018) Multichannel Hankel matrix completion through nonconvex optimization. IEEE J. Selected Topics Signal Processing 12(4):617–632.Crossref, Google Scholar
- (2015) Trajectory data mining: An overview. ACM Trans. Intelligent Systems Tech. 6(3):1–41.Crossref, Google Scholar

