On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants
References
- (2022) On multivariate singular spectrum analysis and its variants. ACM SIGMETRICS Performance Evaluation Rev. 50(1):79–80.Crossref, Google Scholar
- (2020) On principal component regression in a high-dimensional error-in-variables setting. Preprint, submitted October 27, https://arxiv.org/abs/2010.14449v1.Google Scholar
- (2018) Model agnostic time series analysis via matrix estimation. Proc. ACM Measurement Anal. Comput. Systems 2(3):1–39.Crossref, Google Scholar
- (2019) On robustness of principal component regression. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems 32 (NeurIPS 2019) (Curran Associates, Red Hook, NY), 9889–9900.Google Scholar
- (2023) SAMoSSA: Multivariate singular spectrum analysis with stochastic autoregressive noise. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, ed. Adv. Neural Inform. Processing Systems 36 (NeurIPS 2023) (Curran Associates, Red Hook, NY), 8442–8486.Google Scholar
- Anderson BDO, Deistler M (2008) Generalized linear dynamic factor models—a structure theory. Abdallah CT, ed. Proc. 47th IEEE Conf. Decision Control (IEEE, Piscataway, NJ), 1980–1985.Google Scholar
- (2012) Autoregressive models of singular spectral matrices. Automatica 48(11):2843–2849.Crossref, Google Scholar
- (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. J. Appl. Econometrics 29(1):133–160.Crossref, Google Scholar
- (2016) Noisy tensor completion via the sum-of-squares hierarchy. Feldman V, Rakhlin A, Shamir O, eds. 29th Annual Conf. Learn. Theory. Proc. Machine Learn. Res., vol. 49 (PMLR, New York), 417–445.Google Scholar
- (2019) Quasi maximum likelihood estimation of non-stationary large approximate dynamic factor models. Preprint, submitted October 22, https://arxiv.org/abs/1910.09841.Google Scholar
- (2020) Understanding fluctuations through multivariate circulant singular spectrum analysis. Preprint, submitted July 15, https://arxiv.org/abs/2007.07561v1.Google Scholar
- (1986) On the qualitative analysis of experimental dynamical systems. Sarkar S, ed. Nonlinear Phenomena and Chaos (Adam Hilger, Bristol, UK), 113–144.Google Scholar
- (2019) Nonconvex low-rank tensor completion from noisy data. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems 32 (NeurIPS 2019) (Curran Associates, Red Hook, NY), 1863–1874.Google Scholar
- (2015) Keras. Accessed November 15, 2021, https://github.com/fchollet/keras.Google Scholar
- (2012) A quasi-maximum likelihood approach for large, approximate dynamic factor models. Rev. Econom. Statist. 94(4):1014–1024.Crossref, Google Scholar
- (2000) The generalized dynamic-factor model: Identification and estimation. Rev. Econom. Statist. 82(4):540–554.Crossref, Google Scholar
- (2014) The optimal hard threshold for singular values is 4/3. IEEE Trans. Inform. Theory 60(8):5040–5053.Crossref, Google Scholar
- (2002) Advanced spectral method for climatic time series. Rev. Geophysics 40(1):3.1–3.41.Crossref, Google Scholar
- (2001) Analysis of Time Series Structure: SSA and Related Techniques (Chapman and Hall/CRC, New York).Google Scholar
- (2007) Determining the number of factors in the general dynamic factor model. J. Amer. Statist. Assoc. 102(478):603–617.Crossref, Google Scholar
- (2013) Multivariate singular spectrum analysis: A general view and new vector forecasting approach. Internat. J. Energy Statist. 1(1):55–83.Crossref, Google Scholar
- (2018) Singular Spectrum Analysis: Using R (Springer, London).Crossref, Google Scholar
- (2013) Forecasting UK industrial production with multivariate singular spectrum analysis. J. Forecasting 32(5):395–408.Crossref, Google Scholar
- (2011) Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics 76(3):V25–V32.Crossref, Google Scholar
- (1994) Spells of low-frequency oscillations and weather regimes in the Northern Hemisphere. J. Atmospheric Sci. 51(2):210–236.Crossref, Google Scholar
- (2015) Collaborative filtering with graph information: Consistency and scalable methods. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Adv. Neural Inform. Processing Systems 28 (NIPS 2015) (MIT Press, Cambridge, MA), 2107–2115.Google Scholar
- (2020) DeepAR: Probabilistic forecasting with autoregressive recurrent networks. Internat. J. Forecasting 36(3):1181–1191.Crossref, Google Scholar
- (2019) Iterative collaborative filtering for sparse noisy tensor estimation. Caire G, Veeravalli VV, Wagner AB, Zémor G, eds. Proc. 2019 IEEE Internat. Sympos. Inform. Theory (ISIT) (IEEE, Piscataway, NJ), 41–45.Google Scholar
- (2015) Time Series Analysis and Its Applications with R Examples, 3rd ed., Blue Printing (Springer, New York).Google Scholar
- (2002) Forecasting using principal components from a large number of predictors. J. Amer. Statist. Assoc. 97(460):1167–1179.Crossref, Google Scholar
- (2010) Introduction to the non-asymptotic analysis of random matrices. Preprint, submitted November 12, https://arxiv.org/abs/1011.3027v1.Google Scholar
- (2006) All of Nonparametric Statistics (Springer, New York).Google Scholar
- (2008) Regularized non-negative matrix factorization with temporal dependencies for speech denoising. Fletcher J, Loakes D, Goecke R, Burnham D, Wagner M, eds. Proc. Interspeech 2008: 9th Annual Conf. Internat. Speech Comm. Assoc. Incorporating SST 2008 (International Speech Communication Association, Brisbane, Australia), 411–414.Google Scholar
- (2018) Statistically optimal and computationally efficient low rank tensor completion from noisy entries. Preprint, submitted March 19, https://arxiv.org/abs/1711.04934v2.Google Scholar
- (2020) Tensor estimation with nearly linear samples. Preprint, submitted July 1, https://arxiv.org/abs/2007.00736v1.Google Scholar
- (2016) Temporal regularized matrix factorization for high-dimensional time series prediction. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Adv. Neural Inform. Processing Systems 29 (NIPS 2016) (Curran Associates, Red Hook, NY), 847–855.Google Scholar

