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We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-noise observed entries, and performing linear regression to make predictions.
The Theory of Probabilities
Sergei Bernstein. 1946 · 1946
Earlier work this paper cites.
Estimating network edge probabilities by neighborhood smoothing
Yuan Zhang, Elizaveta Levina, and Ji Zhu. 2015 · 1946
Earlier work this paper cites.
A new approach to linear filtering and prediction problems
Rudolph Emil Kalman et al · 1960
Earlier work this paper cites.
Statistical inference for probabilistic functions of finite state Markov chains
Leonard E Baum and Ted Petrie. 1966 · 1966
Earlier work this paper cites.
BEHAVIOR OF SEQUENTIAL PREDICTORS OF BINARY SEQUENCES
Thomas M Cover. 1966 · 1966
Earlier work this paper cites.
Estimation of time series models in the presence of missing data
William Dunsmuir and PM Robinson. 1981 · 1981
Earlier work this paper cites.
Approximate realization based upon an alternative to the Hankel matrix: the Page matrix
A.A.H Damen, P.M.J Van den Hof, and A.K Hajdasinskit. 1982 · 1982
Earlier work this paper cites.
An approach to time series smoothing and forecasting using the EM algorithm
Robert H Shumway and David S Stoffer. 1982 · 1982
Earlier work this paper cites.
Universal coding, information, prediction, and estimation
Jorma Rissanen. 1984 · 1984
Earlier work this paper cites.
Universal prediction of individual sequences
Meir Feder, Neri Merhav, and Michael Gutman. 1992 · 1992
Earlier work this paper cites.
Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber. 1992 · 1992
Earlier work this paper cites.
Time Series Analysis, Forecasting and Control (3rd ed.)
Jenkins Box and Reinsel. 1994 · 1994
Earlier work this paper cites.
Time series analysis . Vol. 2
James Douglas Hamilton. 1994 · 1994
Earlier work this paper cites.
The interactions between ergodic theory and information theory. In IEEE Transactions on Information Theory . Citeseer
Paul C Shields. 1998 · 1998
Earlier work this paper cites.
Estimation of time-varying parameters in statistical models: an optimization approach
Dimitris Bertsimas, David Gamarnik, and John N Tsitsiklis. 1999 · 1999
Earlier work this paper cites.
Analysis of time series structure: SSA and related techniques
Nina Golyandina, Vladimir Nekrutkin, and Anatoly A Zhigljavsky. 2001 · 2001
Earlier work this paper cites.
Singular spectrum analysis for time series with missing data
David H Schoellhamer. 2001 · 2001
Earlier work this paper cites.
Nonnegative matrix factorization with temporal smoothness and/or spatial decorrelation constraints. In Laboratory for Advanced Brain Signal Processing, RIKEN, Tech. Rep
Zhe Chen and Andrzej Cichocki. 2005 · 2005
Earlier work this paper cites.
Matrix completion from a few entries
Raghunandan H Keshavan, Andrea Montanari, and Sewoong Oh. 2010a · 2010
Cited alongside, same era.
Exploiting temporal stability and low-rank structure for localization in mobile networks. In Proceedings of the sixteenth annual international conference on Mobile computing and networking . ACM, 161–172
Swati Rallapalli, Lili Qiu, Yin Zhang, and Yi-Chao Chen. 2010 · 2010
Cited alongside, same era.
Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin. 2010 · 2010
Cited alongside, same era.
Autoregressive process modeling via the lasso procedure
Yuval Nardi and Alessandro Rinaldo. 2011 · 2011
Cited alongside, same era.
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Sahand Negahban and Martin J Wainwright. 2011 · 2011
Cited alongside, same era.
Time Series Analysis and It’s Applications (3rd ed.)
David S. Stoffer Robert H. Shumway. 2015 · 2015
Later among the works it cites.
Improved singular spectrum analysis for time series with missing data
Y Shen, F Peng, and B Li. 2015 · 2015
Later among the works it cites.
Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap
Emmanuel Abbe and Colin Sandon. 2016 · 2016
Later among the works it cites.
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering. In Advances in Neural Information Processing Systems 29 . 2155–2163
Christina E. Lee, Yihua Li, Devavrat Shah, and Dogyoon Song. 2016 · 2016
Later among the works it cites.
Singular spectrum-based matrix completion for time series recovery and prediction
Grigorios Tsagkatakis, Baltasar Beferull-Lozano, and Panagiotis Tsakalides. 2016 · 2016
Later among the works it cites.
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A simpler approach to matrix completion
Benjamin Recht. 2011 · 2011
Cited alongside, same era.
Time series analysis by state space methods . Vol. 38
James Durbin and Siem Jan Koopman. 2012 · 2012
Cited alongside, same era.
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity
Loh Po-ling and Martin J Wainwright. 2012 · 2012
Cited alongside, same era.
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation. In Advances in Neural Information Processing Systems . 692–700
Edo M Airoldi, Thiago B Costa, and Stanley H Chan. 2013 · 2013
Cited alongside, same era.
A tensor spectral approach to learning mixed membership community models. In Conference on Learning Theory . 867–881
Animashree Anandkumar, Rong Ge, Daniel Hsu, and Sham Kakade. 2013 · 2013
Cited alongside, same era.
Time series: theory and methods
Peter J Brockwell and Richard A Davis. 2013 · 2013
Cited alongside, same era.
1-bit matrix completion
Mark A Davenport, Yaniv Plan, Ewout van den Berg, and Mary Wootters. 2014 · 2014
Cited alongside, same era.
A Unified Framework for Missing Data and Cold Start Prediction for Time Series Data. In Advances in neural information processing systems Time Series Workshop
Christopher Xie, Alex Talk, and Emily Fox. 2016 · 2016
Later among the works it cites.
Temporal regularized matrix factorization for high-dimensional time series prediction. In Advances in neural information processing systems . 847–855
Hsiang-Fu Yu, Nikhil Rao, and Inderjit S Dhillon. 2016 · 2016
Later among the works it cites.
Censored Demand Estimation in Retail
Muhammad J Amjad and Devavrat Shah. 2017 · 2017
Later among the works it cites.
Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. 2017 · 2017
Later among the works it cites.
Linear and conic programming estimators in high dimensional errors-in-variables models
Alexandre Belloni, Mathieu Rosenbaum, and Alexandre B Tsybakov. 2017 · 2017
Later among the works it cites.
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation. In Advances in Neural Information Processing Systems . 4718–4729
Christian Borgs, Jennifer Chayes, Christina E Lee, and Devavrat Shah. 2017 · 2017
Later among the works it cites.
Cocolasso for high-dimensional error-in-variables regression
Abhirup Datta and Hui Zou. 2017 · 2017
Later among the works it cites.
Efficient Bayesian estimation from few samples: community detection and related problems. In Foundations of Computer Science (FOCS), 2017 IEEE 58th Annual Symposium on . IEEE, 379–390
Samuel B Hopkins and David Steurer. 2017 · 2017
Later among the works it cites.
Statistical and computational guarantees for the Baum-Welch algorithm
Fanny Yang, Sivaraman Balakrishnan, and Martin J Wainwright. 2017 · 2017
Later among the works it cites.
Supervised Learning in High Dimensions via Matrix Estimation
Anish Agarwal, Devavrat Shah, Dennis Shen, and Dogyoon Song. 2018 · 2018
Closest in time.
Matrix completion from noisy entries
Raghunandan H Keshavan, Andrea Montanari, and Sewoong Oh. 2010b · 2078
Closest in time.
The power of convex relaxation: Near-optimal matrix completion
Emmanuel J Candès and Terence Tao. 2010 · 2080
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