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Time series prediction has been a long-standing research topic and an essential application in many domains.
N. Golyandina, V. Nekrutkin, and A. A. Zhigljavsky, Analysis of Time Series Structure: SSA and Related Techniques . CRC press, 2001
2001
Earlier work this paper cites.
T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Review , vol. 51, no. 3, pp. 455–500, 2009
2009
Earlier work this paper cites.
L. Xiong, X. Chen, T.-K. Huang, J. Schneider, and J. G. Carbonell, “Temporal collaborative filtering with bayesian probabilistic tensor factorization,” in Proceedings of the 2010 SIAM International Conference on Data Mining , 2010, pp. 211–222
2010
Earlier work this paper cites.
B. Recht, M. Fazel, and P. A. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM Review , vol. 52, no. 3, pp. 471–501, 2010
2010
Earlier work this paper cites.
J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM Journal on Optimization , vol. 20, no. 4, pp. 1956–1982, 2010
2010
Earlier work this paper cites.
Y. Zhang and Z. Lu, “Penalty decomposition methods for rank minimization,” in Advances in Neural Information Processing Systems , 2011, pp. 46–54
2011
Earlier work this paper cites.
S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain, “Gaussian processes for time-series modelling,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , vol. 371, p. 20110550, 2013
2013
Earlier work this paper cites.
M. Figueiredo, B. Ribeiro, and A. de Almeida, “Electrical signal source separation via nonnegative tensor factorization using on site measurements in a smart home,” IEEE Transactions on Instrumentation and Measurement , vol. 63, no. 2, pp. 364–373, 2013
2013
Earlier work this paper cites.
J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 1, pp. 208–220, 2013
2013
Earlier work this paper cites.
Y. Hu, D. Zhang, J. Ye, X. Li, and X. He, “Fast and accurate matrix completion via truncated nuclear norm regularization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 9, pp. 2117–2130, Sep. 2013
2013
Earlier work this paper cites.
K. Chen, H. Dong, and K.-S. Chan, “Reduced rank regression via adaptive nuclear norm penalization,” Biometrika , vol. 100, no. 4, pp. 901–920, 2013
2013
Earlier work this paper cites.
M. T. Bahadori, Q. R. Yu, and Y. Liu, “Fast multivariate spatio-temporal analysis via low rank tensor learning,” in Advances in Neural Information Processing Systems , 2014, pp. 3491–3499
2014
Earlier work this paper cites.
Y. Chen and Y. Chi, “Robust spectral compressed sensing via structured matrix completion,” IEEE Transactions on Information Theory , vol. 60, no. 10, pp. 6576–6601, 2014
2014
Cited alongside, same era.
L. Li, X. Su, Y. Zhang, Y. Lin, and Z. Li, “Trend modeling for traffic time series analysis: An integrated study,” IEEE Transactions on Intelligent Transportation Systems , vol. 16, no. 6, pp. 3430–3439, 2015
2015
Cited alongside, same era.
C. Lu, C. Zhu, C. Xu, S. Yan, and Z. Lin, “Generalized singular value thresholding,” in AAAI Conference on Artificial Intelligence (AAAI) , 2015
2015
Cited alongside, same era.
H.-F. Yu, N. Rao, and I. S. Dhillon, “Temporal regularized matrix factorization for high-dimensional time series prediction,” in Advances in Neural Information Processing Systems , 2016, pp. 847–855
2016
Cited alongside, same era.
J. Gillard and K. Usevich, “Structured low-rank matrix completion for forecasting in time series analysis,” International Journal of Forecasting , vol. 34, no. 4, pp. 582–597, 2018
2018
Later among the works it cites.
A. Agarwal, M. J. Amjad, D. Shah, and D. Shen, “Model agnostic time series analysis via matrix estimation,” Proceedings of the ACM on Measurement and Analysis of Computing Systems , vol. 2, no. 3, pp. 1–39, 2018
2018
Later among the works it cites.
T. Yokota, B. Erem, S. Guler, S. K. Warfield, and H. Hontani, “Missing slice recovery for tensors using a low-rank model in embedded space,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8251–8259
2018
Later among the works it cites.
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” in Advances in Neural Information Processing Systems , 2019, pp. 5244–5254
2019
Later among the works it cites.
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H. Tan, Y. Wu, B. Shen, P. J. Jin, and B. Ran, “Short-term traffic prediction based on dynamic tensor completion,” IEEE Transactions on Intelligent Transportation Systems , vol. 17, no. 8, pp. 2123–2133, 2016
2016
Cited alongside, same era.
R. Yu, S. Zheng, A. Anandkumar, and Y. Yue, “Long-term forecasting using tensor-train RNNs,” Arxiv , 2017
2017
Cited alongside, same era.
M. R. de Araujo, P. M. P. Ribeiro, and C. Faloutsos, “Tensorcast: Forecasting with context using coupled tensors,” in IEEE International Conference on Data Mining (ICDM) , 2017, pp. 71–80
2017
Cited alongside, same era.
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice , 2nd ed. OTexts, 2018
2018
Cited alongside, same era.
C. Faloutsos, J. Gasthaus, T. Januschowski, and Y. Wang, “Forecasting big time series: old and new,” Proceedings of the VLDB Endowment , vol. 11, no. 12, pp. 2102–2105, 2018
2018
Cited alongside, same era.
P. Jing, Y. Su, X. Jin, and C. Zhang, “High-order temporal correlation model learning for time-series prediction,” IEEE Transactions on Cybernetics , vol. 49, no. 6, pp. 2385–2397, 2018
2018
Cited alongside, same era.
G. Lai, W.-C. Chang, Y. Yang, and H. Liu, “Modeling long-and short-term temporal patterns with deep neural networks,” in ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 95–104
2018
Cited alongside, same era.
R. Sen, H.-F. Yu, and I. S. Dhillon, “Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting,” in Advances in Neural Information Processing Systems , 2019, pp. 4838–4847
2019
Later among the works it cites.
2019
Later among the works it cites.
I. Markovsky, Low Rank Approximation Algorithms, Implementation, Applications , 2nd ed. Springer, 2019
2019
Later among the works it cites.
S. Zhang and M. Wang, “Correction of corrupted columns through fast robust hankel matrix completion,” IEEE Transactions on Signal Processing , vol. 67, no. 10, pp. 2580–2594, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Closest in time.
X. Chen, J. Yang, and L. Sun, “A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation,” Transportation Research Part C: Emerging Technologies , 2020
2020
Closest in time.