Matrix factorization for multivariate time series analysis

Matrix factorization is a powerful data analysis tool. It has been used in multivariate time series analysis, leading to the decomposition of the series in a small set of latent factors. However, little is known on the statistical performances of matrix factorization for time series. In this paper,...

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Bibliographic Details
Published in:Electronic journal of statistics 2019-01, Vol.13 (2), p.4346-4366
Main Authors: Alquier, Pierre, Marie, Nicolas
Format: Article
Language:English
Subjects:
Statistics
Statistics Theory
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Summary:Matrix factorization is a powerful data analysis tool. It has been used in multivariate time series analysis, leading to the decomposition of the series in a small set of latent factors. However, little is known on the statistical performances of matrix factorization for time series. In this paper, we extend the results known for matrix estimation in the i.i.d setting to time series. Moreover, we prove that when the series exhibit some additional structure like periodicity or smoothness, it is possible to improve on the classical rates of convergence.
ISSN:1935-7524
1935-7524
DOI:10.1214/19-EJS1630