Matrix Factorization for Multivariate Time Series Analysis

Abstract : 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.
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Contributor : Nicolas Marie <>
Submitted on : Wednesday, November 6, 2019 - 8:22:02 PM
Last modification on : Monday, November 11, 2019 - 1:18:10 AM


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Pierre Alquier, Nicolas Marie. Matrix Factorization for Multivariate Time Series Analysis. Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2019, 13 (2), pp.4346-4366. ⟨10.1214/19-EJS1630⟩. ⟨hal-02068455v2⟩



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